Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Ouache, Rachid
Doctor of Philosophy - PhD
University of British Columbia
The building industry has been facing significant challenges, including high quality, sustainability, cost-effectiveness and safety. Fire risk poses a significant threat and impacts on public safety, property, and the environment. Canada recorded 0.5 million fires in 10 years, which caused 15,000 fatalities, a direct loss of $7.5 billion, 1,200 children were injured, and 1.2 million Canadians were affected directly. In British Columbia (BC), 55% of the fires were associated with multi-unit residential buildings, which are being increasingly built with smart and green features for a positive impact, however, these features may negatively impact from a fire’s perspective. This research developed an integrated fire risk management framework using machine learning algorithms for multi-unit residential buildings integrating smart and green features (SG-MURBs). Five phases were incorporated in the framework to enhance fire prevention, protection, and intervention strategies: (I) investigated state-of-the-practice to explore fire risk management practices (II) Identified key potential fire risk and safety-related factors considering relative frequencies and their related impacts (III) Developed benchmarks to set the acceptability levels and determine the critical factors accordingly (IV) Developed models to predict the potential impacts of fire incidents in SG-MURBs, and finally (V) Generated optimal solutions and optimized multidimensional fire impacts. The developed framework was applied and tested for seven cities in BC, Canada. The results discerned the key potential contributing factors to fire incidents covering common, smart, and green factors, including 40 ignition sources, 28 human errors, and 36 combustible materials. Using the developed benchmarks, Vancouver, Kelowna, and Kamloops were found the top critical cities with 58%, 37%, and 47% of very high levels of ignition sources, human errors, and combustible materials, respectively. The most suitable methods for fire control and fire suppression materials for the top critical combustible materials were determined. The ANN models were found to outperform classifiers with prediction abilities of 65%, 72%, 85%, and 99% in predicting related potential impacts. Moreover, the optimal set of fire safety strategies were determined using genetic algorithms to enhance fire risk management. The developed framework will help decision-makers guide policies and enhance investments for specific fire prevention, protection, and intervention strategies accordingly.
AN INTEGRATED FIRE RISK MANAGEMENT OF MULTI-UNIT RESIDENTIAL BUILDINGS WITH SMART AND GREEN FEATURES: MACHINE LEARNING-BASED FRAMEWORK by Rachid Ouache Ph.D., University of Malaysia Pahang – Malaysia (2016) M.A.Sc., University of Batna – Algeria (2011) B.Sc. (Eng.), University of Batna – Algeria (2009) A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2021 © Rachid Ouache, 2021ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: AN INTEGRATED FIRE RISK MANAGEMENT OF MULTI-UNIT RESIDENTIAL BUILDINGS WITH SMART AND GREEN FEATURES: MACHINE LEARNING-BASED FRAMEWORK submitted by Rachid Ouache in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil Engineering Examining Committee: Rehan Sadiq, Ph.D. (School of Engineering: Civil Engineering) Supervisor Kasun Hewage, Ph.D. (School of Engineering: Civil Engineering) Co-Supervisor Shahria Alam, Ph.D. (School of Engineering: Civil Engineering) Supervisory Committee Member Zheng Liu, Ph.D. (School of Engineering: Civil Engineering) Supervisory Committee Member Mina Hoorfar, Ph.D. (School of Engineering: Mechanical Engineering) University Examiner Zhi Chen, Ph.D. (Concordia University) External Examiner iii ABSTRACT The building industry has been facing significant challenges, including high quality, sustainability, cost-effectiveness and safety. Fire risk poses a significant threat and impacts on public safety, property, and the environment. Canada recorded 0.5 million fires in 10 years, which caused 15,000 fatalities, a direct loss of $7.5 billion, 1,200 children were injured, and 1.2 million Canadians were affected directly. In British Columbia (BC), 55% of the fires were associated with multi-unit residential buildings, which are being increasingly built with smart and green features for a positive impact, however, these features may negatively impact from a fire’s perspective. This research developed an integrated fire risk management framework using machine learning algorithms for multi-unit residential buildings integrating smart and green features (SG-MURBs). Five phases were incorporated in the framework to enhance fire prevention, protection, and intervention strategies: (I) investigated state-of-the-practice to explore fire risk management practices (II) Identified key potential fire risk and safety-related factors considering relative frequencies and their related impacts (III) Developed benchmarks to set the acceptability levels and determine the critical factors accordingly (IV) Developed models to predict the potential impacts of fire incidents in SG-MURBs, and finally (V) Generated optimal solutions and optimized multidimensional fire impacts. The developed framework was applied and tested for seven cities in BC, Canada. The results discerned the key potential contributing factors to fire incidents covering common, smart, and green factors, including 40 ignition sources, 28 human errors, and 36 combustible materials. Using the developed benchmarks, Vancouver, Kelowna, and Kamloops were found the top critical cities with 58%, 37%, and 47% of very high levels of ignition sources, human errors, iv and combustible materials, respectively. The most suitable methods for fire control and fire suppression materials for the top critical combustible materials were determined. The ANN models were found to outperform classifiers with prediction abilities of 65%, 72%, 85%, and 99% in predicting related potential impacts. Moreover, the optimal set of fire safety strategies were determined using genetic algorithms to enhance fire risk management. The developed framework will help decision-makers guide policies and enhance investments for specific fire prevention, protection, and intervention strategies accordingly. v LAY SUMMARY Smart-Green multi-unit residential buildings (SG-MURBs) face severe fire challenges globally, causing catastrophic consequences on people's safety, properties, and the environment. This study developed an integrated fire risk management framework to identify, analyze, evaluate, and prioritize fire risk and safety factors to enhance prevention, protection, and intervention strategies for SG-MURBs. The framework integrated machine learning algorithms, including artificial neural networks and genetic algorithms, to develop comprehensive models and solutions. The developed framework was demonstrated through case studies in British Columbia, Canada. The results highlighted the critical risks and safety factors in SG-MURBs. Robust machine learning algorithms were developed considering several predictors, covering ignition sources and combustible materials, with responses like fire damage and dollar losses. The results selected the best combination of scenarios for fire protection and potential intervention strategies. This study is essential in assessing and controlling fire incidents, and in assisting decision-makers to incorporate fire safety strategies accordingly. vi PREFACE I, Rachid Ouache, intellectualized and developed the entire thesis; conceptualization, methodology, visualization, and writing under the supervision of Dr. Rehan Sadiq and Dr. Kasun Hewage. Five journal articles, book chapters, and a conference proceeding are produced. The first journal article focused on developing an approach to support fire prevention through investigating the potential ISFs-FIs in SG-MURBs, establishing benchmarks for the relative frequencies and the related dollar loss to evaluate ISFs-FIs. Besides, the article developed ANN and classification models to predict fire impacts. The second journal article developed a HEFs-FIs approach to identify, analyze, and evaluate human error factors supporting fire prevention. Evidential reasoning and machine learning algorithms were used to assess and predict HEFs-FIs effectively. An approach to assess combustible materials in SG-MURBs is developed in the third journal article to enhance fire protection and determine the appropriate fire suppression materials and methods of fire control accordingly. The fourth journal article has developed an approach to investigate essential fire strategies in SG-MURBs covering fire strategies assessment to support mainly fire protection and intervention. Moreover, machine-learning models were developed to predict fire impacts and optimize the combination set of predictors that minimize potential impacts. The fifth journal article used deep learning to integrate fire risk and related safety factors to enhance fire prevention, protection and intervention strategies. A conference proceeding and book chapter were developed as extensions of the second and the fourth articles. Details of the produced papers are provided below: 1. Ouache, R., Gyan, C., Hewage, K., Sadiq, R. (2020) An Integrated Risk Assessment Framework for Fire Ignition Sources in Smart-Green Multi-Unit Residential Buildings. International Journal of System Assurance Engineering and Management, IJSA-D-20-00191. vii 2. Ouache, R., Bakhtavara, E., Guangji H., Hewage, K., Sadiq, R. (2020) Machine Learning-based Assessment Framework to Investigate Quantitatively Human Error Factors-induced Fire Incidents in MURBs. Fire Safety Journal, FISJ_2020_228. 3. Ouache, R., Gyan, C., Hewage, K., Sadiq, R. (2020) Combustible Materials Ignability in Smart-Green MURBs: A Fire Risk Assessment and Protection Framework. Fire Technology Journal, FIRE-D-20-00307. 4. Ouache, R., Nahid, K., Hewage, K., Sadiq, R. (2021) Performance Investigation of Fire Protection and Intervention Strategies: Artificial Neural Network-based Assessment Framework. Journal of Building Engineering, https://doi.org/10.1016/j.jobe.2021.102439. 5. Ouache, R., Hewage K., Sadiq R. (2021). Deep learning to enhance fire risk assessment in smart-green multi-unit residential buildings (to be submitted to Journal of Building Engineering, Elsevier) 6. Ouache, R., Guangji, H., Hewage, K., Sadiq, R. (2018) An act and omission contributor-based fire probabilistic analysis for residential buildings. Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems in conjunction with Intelligent Systems Workshop, SCIS&ISIS, JAPAN. 7. Ouache, R., Amin, M., Hewage, K., Sadiq, R. (2021). A data-driven model for fire safety strategies assessment using artificial neural networks and genetic algorithms (An Accepted Book Chapter: Nature-Inspired Computing Paradigms in Systems: RAMS+C & PHM, Elsevier) viii Table of Contents ABSTRACT ......................................................................................................................................... iii LAY SUMMARY ................................................................................................................................. v PREFACE ............................................................................................................................................ vi LIST OF TABLES ............................................................................................................................. xii LIST OF FIGURES .......................................................................................................................... xiii LIST OF ABBREVIATIONS .......................................................................................................... xvii ACKNOWLEDGEMENTS ............................................................................................................ xviii DEDICATION .................................................................................................................................. xxx CHAPTER 1: INTRODUCTION ....................................................................................................... 1 1.1. Background ................................................................................................................................. 1 1.2. Research Motivation ................................................................................................................... 4 1.3. Research Gaps ............................................................................................................................. 5 1.4. Objectives and Outcomes ............................................................................................................ 9 1.5. Metalanguage ............................................................................................................................ 10 1.6. Thesis Organization .................................................................................................................. 12 CHAPTER 2: RESEARCH METHODOLOGY ............................................................................. 15 2.1. Phase I – Literature Review, Content Analysis, and Data Collection ....................................... 15 2.2. Phase II – Potential Factor Identification and Mitigation Techniques ...................................... 15 2.3. Phase III – Fire Occurrence and Impact Analysis ..................................................................... 16 2.4. Phase IV – Correlation and Prediction Analyses ...................................................................... 18 2.5. Phase V – Optimization-based Fire Response Strategies and Best Management Practices ...... 18 2.6. Framework Application ............................................................................................................ 18 CHAPTER 3: LITERATURE REVIEW ......................................................................................... 20 3.1. Smart-Green Multi-Unit Residential Buildings ........................................................................ 20 3.1.1. Construction materials.................................................................................................... 21 3.1.2. Smart features ................................................................................................................. 23 ix 3.1.3. Green features ................................................................................................................ 25 3.2. Fire Risk Management .............................................................................................................. 31 3.2.1. Fire risk identification .................................................................................................... 34 3.2.2. Fire risk analysis............................................................................................................. 48 3.2.3. Fire risk evaluation ......................................................................................................... 49 3.2.4. Fire safety strategies ....................................................................................................... 51 3.3. Challenges and Sources of Uncertainties .................................................................................. 52 3.4. Quantification of Uncertainties ................................................................................................. 54 CHAPTER 4: AN INTEGRATED RISK ASSESSMENT APPROACH FOR FIRE IGNITION SOURCES ........................................................................................................................................... 58 4.1. Background ............................................................................................................................... 58 4.2. Methodology ............................................................................................................................. 59 4.2.1. Categorization and identification of potential ISFs-FIs ................................................. 60 4.2.2. Benchmarking: analysis and evaluation ......................................................................... 61 4.2.3. Prediction analysis: classification and ANN-based regression modelling ..................... 62 4.3. Results and Discussions ............................................................................................................ 72 4.3.1. Potential fire ignition source factors .............................................................................. 72 4.3.2. Benchmarking analysis and evaluation of ISFs-FIs ....................................................... 75 4.3.3. Correlation and association analysis .............................................................................. 80 4.3.4. Classification results ...................................................................................................... 83 4.3.5. Artificial Neural Network results ................................................................................... 86 4.4. Summary ................................................................................................................................... 93 CHAPTER 5: QUANTITATIVE ASSESSMENT OF HUMAN ERROR FACTORS ................ 94 5.1. Background ............................................................................................................................... 94 5.2. Methodology ............................................................................................................................. 95 5.2.1. Identification and analysis of human error factors that induce fire incidents ................ 96 5.2.2. A benchmark and evidential reasoning-based HEFs-FIs assessment ............................. 98 5.2.3. Prediction analysis: classification and artificial neural network-based regression ...... 102 x 5.3. Results and Discussions .......................................................................................................... 105 5.3.1. Analysis of human error factors that induce fire incidents in SG-MURBs .................. 105 5.3.2. Benchmarking of HEFs-FIs ......................................................................................... 108 5.3.3. Application of evidential reasoning ............................................................................. 111 5.3.4. HEFs-FIs-based Prediction results ............................................................................... 114 5.4. Summary ................................................................................................................................. 124 CHAPTER 6: FIRE RISK ASSESSMENT AND PROTECTION .............................................. 126 6.1. Background ............................................................................................................................. 126 6.2. Methodology ........................................................................................................................... 127 6.2.1. Categorization and identification of potential CMs-FIs ............................................... 129 6.2.2. Benchmarking: analysis and evaluation ....................................................................... 129 6.2.3. Selection of fire suppression materials and methods of fire control ............................ 130 6.2.4. Predicting the impacts of fire incidents ........................................................................ 133 6.3. Results and Discussions ......................................................................................................... 134 6.3.1. Potential combustible materials-induced fire incidents ................................................ 134 6.3.2. Benchmarking .............................................................................................................. 138 6.3.3. Evaluation of CMs-FIs ................................................................................................. 141 6.3.4. Selection of fire suppression materials and methods of fire control ............................ 144 6.3.5. Prediction of fire impacts based on ANN .................................................................... 152 6.4. Summary ................................................................................................................................ 157 CHAPTER 7: A DATA-DRIVEN MODELS FOR FIRE SAFETY ASSESSMENT ................ 159 7.1. Background ............................................................................................................................. 159 7.2. Methodology ........................................................................................................................... 160 7.2.1. Investigation of protection and intervention strategies ................................................ 161 7.2.2. ANN-based prediction.................................................................................................. 163 7.2.3. Optimization analysis ................................................................................................... 164 xi 7.3. Results and Discussions .......................................................................................................... 167 7.3.1. Investigating the potential fire protection and intervention strategies with impacts .... 167 7.3.2. Association and correlation analyses among predictors with responses ...................... 171 7.3.3. Prediction analysis........................................................................................................ 176 7.3.4. Optimization analysis results ....................................................................................... 179 7.4. Summary ................................................................................................................................. 184 CHAPTER 8: CONCLUSIONS AND RECOMMENDATIONS ................................................ 186 8.1. Summary and Conclusions ...................................................................................................... 186 8.1.1. Ignition sources assessment ......................................................................................... 186 8.1.2. Human reliability: human error analysis ...................................................................... 186 8.1.3. Fire protection: assessment, selection, and prediction ................................................. 187 8.1.4. Safety strategies management ...................................................................................... 188 8.2. Originality and Contributions................................................................................................. 189 8.2.1. Potential risk and safety factor identification ............................................................... 189 8.2.2. Benchmarks development ............................................................................................ 189 8.2.3. Prediction models ......................................................................................................... 190 8.2.4. Optimization-based fire response strategies and best management practices .............. 191 8.3. Limitations ............................................................................................................................. 191 8.3.1. Focus on SG-MURBs................................................................................................... 191 8.3.2. Data availability and uncertainties ............................................................................... 192 8.4. Future Research ...................................................................................................................... 192 REFERENCES ................................................................................................................................. 194 APPENDICES .................................................................................................................................. 220 xii LIST OF TABLES Table 3-1: Rating systems for green buildings in different countries around the world ...................... 25 Table 3-2: Comparison of the Assessment criteria between the most used rating systems ................. 26 Table 3-3: Potential hazards of green building features: Low (L), Medium (M), High (H) ................ 28 Table 3-4: Related fire incidents with green features around the world .............................................. 30 Table 3-5: Related studies on buildings fire protection ....................................................................... 33 Table 3-6: A summary of human error factors induced fire incidents ................................................. 40 Table 3-7: Potential human error factors in SG-MURBs ..................................................................... 41 Table 3-8: Combustible materials in SG-MURBs ............................................................................... 44 Table 3-9: A summary of studies investigated fire safety strategies .................................................... 47 Table 3-10: Comparison between ANN and most known quantitative risk methods .......................... 57 Table 4-1: Relative frequencies (per year) of ISFs-FIs in seven cities ................................................ 73 Table 4-2: Best fitted distributions for relative frequencies of ISFs-FIs .............................................. 76 Table 4-3: Best fitted distributions for total dollar loss of ISFs-FIs .................................................... 77 Table 4-4: Levels of relative frequency, total dollar loss, and risk-based levels of ISFs-FIs for seven cities ..................................................................................................................................................... 78 Table 5-1: Human error factors that cause fire incidents in SG-MURBs ............................................ 97 Table 5-2: The determined levels of the predictors for the seven cities ............................................. 103 Table 5-3: Attributes and factors of fire origin in SG-MURBs ......................................................... 104 Table 5-4: Relative frequencies of HEFs-FIs in seven cities ............................................................. 106 Table 5-5: Best fit probability distributions for HEFs-FIs ................................................................. 107 Table 5-6: Relative frequency-based grades and criticality analysis of all the HEFs-FI at the seven cities ................................................................................................................................................... 109 Table 5-7: Degrees of belief, importance weights, and the remaining probability mass unassigned (RPMU) to individual grades of the HEFs-FIs in BC ........................................................................ 113 Table 6-1: Detailed attributes for impacts of fire incidents ................................................................ 128 Table 6-2: Suitability of extinguishers to different types of fires and their possible impacts ............ 132 Table 6-3: Combustible materials in SG-MURBs with relative frequencies in seven cities ............. 136 Table 6-4: Best fitted distributions for relative frequencies of CMs-FIs ........................................... 139 Table 6-5: Best fitted distributions for total dollar loss of CMs-FIs .................................................. 140 Table 6-6: Levels of CMs-FIs for relative frequency, total dollar loss, and risk levels ..................... 143 Table 7-1: Details of the five predictors with their factors for fire impacts ....................................... 162 Table 7-2: Details of the ire impacts responses .................................................................................. 164 Table 7-3: Training parameters of the 'trainbr' training function ....................................................... 166 xiii LIST OF FIGURES Figure 1-1: Synthesis of chapters and objectives ................................................................................. 14 Figure 2-1: An integrated framework for fire risk management in SG-MURBs ................................. 17 Figure 3-1: Exploring smart features with the relative frequency of related ignition source systems . 24 Figure 3-2: Fire incidents in the USA due to photovoltaic (PV) systems ............................................ 29 Figure 3-3: Fire risk management framework with the related uncertainties ...................................... 32 Figure 3-4: Potential risk and safety factors identification with possible fire impacts ........................ 34 Figure 3-5: Top 50 cities with total fire incidents in all types of residential buildings and SG-MURBs between 2005 and 2015 ........................................................................................................................ 35 Figure 3-6: Percentages of ignition sources categories in all types of residential buildings and SG-MURBs in Canada between 2005 and 2015. ....................................................................................... 37 Figure 3-7: Fire ignition sources in smart and green multi-unit residential buildings ......................... 38 Figure 3-8: Percentages of HEFs-FIs for all types of residential buildings and SG-MURBs in Canada between 2005 and 2015 ........................................................................................................................ 39 Figure 3-9: Percentages of CMs-FIs for all types of residential buildings and SG-MURBs in Canada between 2005 and 2015 ........................................................................................................................ 43 Figure 3-10: Percentages of MFC for all types of residential buildings and SG-MURBs in Canada between 2005 and 2015 ........................................................................................................................ 45 Figure 3-11: Investigating factors of the discovery of fire and actions taken ...................................... 46 Figure 3-12: Response strategies for fire risk treatment ...................................................................... 52 Figure 4-1: Integrated risk assessment approach for fire ignition sources in Smart-Green Multi-Unit Residential Buildings ........................................................................................................................... 60 Figure 4-2: Classification-based machine learning for fire ignition sources analysis .......................... 64 Figure 4-3: Artificial neural network approach for fire ignition sources analysis ............................... 70 Figure 4-4: Relative frequencies of ISFs-FIs attributes for the seven cities, BC ................................. 74 Figure 4-5: Dollar loss-percentages of ISFs-FIs attributes in the seven cities, BC .............................. 75 Figure 4-6: Proportions of risk levels in the seven cities, BC .............................................................. 79 Figure 4-7: Pareto results of the predictor AC, the responses PC and NC, and their association ........ 81 Figure 4-8: Pareto results of the predictor SE and its association with the responses PC, NC, and CFE .............................................................................................................................................................. 82 Figure 4-9: Performance of classifiers with confusion matrix and ROC curve of ensemble boosted trees classifier ....................................................................................................................................... 84 Figure 4-10: Performance of nature of casualties-based classifiers with confusion matrix and ROC curve of a coarse tree classifier ............................................................................................................ 85 xiv Figure 4-11: Results of the BR-based ANN for prediction of fire impacts.......................................... 87 Figure 4-12: Results of the ANN-based Simulink model for fire impacts ........................................... 88 Figure 5-1: Approach for assessing human error factors that induce fire incidents in SG-MURBs .... 95 Figure 5-2: Benchmarking for the first 19 human error factors that cause fire incidents .................. 108 Figure 5-3: Comparison between grades of HEFs that cause fire incidents....................................... 110 Figure 5-4: Assessment of HEFs-FIs that cause fire incidents in BC’s SG-MURBs ........................ 112 Figure 5-5: Pareto charts of three predictors and their association with HEFs-FIs ............................ 115 Figure 5-6: Fire incidents in different building areas with LP, YP, and LP predictors ...................... 116 Figure 5-7: Performance of HEFs-based classifiers with confusion matrix and ROC curve of the medium tree classifier ........................................................................................................................ 118 Figure 5-8: Results of the BR-based ANN comparing with LM and SCG for prediction of fire origin and ignition sources-based human factors ......................................................................................... 120 Figure 5-9: Results of ANN- using BR algorithm for prediction of fire origin and ignition sources-based human factors ........................................................................................................................... 121 Figure 6-1: Fire protection approach for Smart-Green Multi-Unit Residential Buildings ................. 128 Figure 6-2: General green features of SG-MURBs ............................................................................ 137 Figure 6-3: Percentages and levels of relative frequency-based CMs-FIs in the cities ...................... 142 Figure 6-4: Green features and their combustibility levels ................................................................ 144 Figure 6-5: Appropriate fire suppression materials for CMs-FIs in SG-MURBs .............................. 145 Figure 6-6: Assessment of appropriate extinguishing systems for GCMs-FIs................................... 147 Figure 6-7: Notched box plot-based distributions among the CMs, methods of fire control and different impacts................................................................................................................................. 149 Figure 6-8: Contour Plot for response variables with two predictor variables ................................... 150 Figure 6-9: Dollar loss with methods of fire control .......................................................................... 151 Figure 6-10: Results of Artificial Neural Network using the LM-based algorithm ........................... 153 Figure 6-11: Artificial neural network-based Simulink diagram ....................................................... 154 Figure 7-1: Integrated approach to investigate, predict, and optimize fire safety strategies .............. 161 Figure 7-2: Process for fire strategies assessment in SG-MURBs ..................................................... 165 Figure 7-3: Transmission of alarm and the taken actions for the first 100 investigated fire incidents ............................................................................................................................................................ 169 Figure 7-4: Fire damages and total dollar loss of the first 100 investigated fire incidents ................ 170 Figure 7-5: Association of the ID and TAFD with the total dollar loss ............................................. 172 Figure 7-6: Identification and association of the PSAD with total dollar loss and fire damages ....... 173 Figure 7-7: Identification and association of the action taken with the total dollar loss .................... 174 xv Figure 7-8: A contour plot for content dollar loss and several predictors .......................................... 175 Figure 7-9: R-values of ANN model with LM, BR, and SCG algorithms ......................................... 176 Figure 7-10: Results of the BR-based ANN for prediction of fire impacts........................................ 177 Figure 7-11: ANN-based Simulink model using BR algorithm for prediction of fire impacts efficiently ........................................................................................................................................... 178 Figure 7-12: Main effects for BDL and multiple response prediction optimizations ........................ 179 Figure 7-13: Results of genetic algorithm-based optimization .......................................................... 181 xvi LIST OF ABBREVIATIONS AC Age-group Categories AIC Akaike Information Criteria ALARP As Low As Reasonably Practicable ANN Artificial Neural Network AT Action Taken BC British Columbia BDL Building Dollar Loss BR Bayesian Regularization BRE Building Research Establishment BREEAM Building Research Establishment Environmental Assessment Method CC Contributing Condition CCMs Common Combustible Materials CDF Cumulative Distribution Functions CDL Content Dollar Loss CFE Cause Failure Escape CMs Combustible Materials CMs-FIs Combustible Materials that ignited in Fire Incidents DE Damage Extension EP Elder People ER Evidential Reasoning EsMF Extinguishers for Manual Firefighting ETA Event Tree Analysis FD Fire Damages FE Fire Extension FRA Fire Risk Assessment FSMs Fire Suppression Materials FTA Fault Tree Analysis GBCA Green Building Council of Australia GBs Green Buildings GN Gray Number HEFs-FI Human Error Factors-Induced Fire Incidents H-HEFs High grade-based Human Error Factors HI Hazard Identification HK-BEAM Hong Kong Building Environmental Assessment Method ICOF Ignition of Clothing or Other Fabrics ID Initial Detection ISFs-FIs Ignition Source Factors-Induced Fire Incidents JaGBC Japan Green Building Council LA Likelihood Analysis LEED Leadership in Energy and Environmental Design L-HEFs Low grade-based Human Error Factors LM Lavenberg-Marquardt LP Lone Parent MFC Methods of Fire Control MFCE-G Method of Fire Control and Extinguishment Group xvii M-HEFs Medium grade-based Human Error Factors MURBs Multi-Unit Residential Buildings NC Nature of casualties NFID National Fire Information Database ORE Occupant Response/ Evacuation PC Possible Consequence PDF Probability Distribution Functions PSAD Performance of Smoke Alarm Device QFRA Quantitative Fire Risk Assessment RA Risk Analysis RE Risk Evaluation SBs Smart Buildings SCG Scaled Conjugate Gradient SE Sex SGFs Smart and Green Features SG-MURBs Smart-Green Multi-Unit Residential Buildings SP Sprinkler Protection TAFD Transmission of Alarm to Fire Department TDL Total Dollar Loss TFMI Type of fabric or material that might be ignited VH-HEFs Very High grade-based human error factors VL-HEFs Very Low grade-based human error factors YP Young people xviii ACKNOWLEDGEMENTS First of all, I would like to praise and thank the almighty God for His grace, strength, sustenance, countless blessings, and above all, His faithfulness and love from the beginning of my life up to this doctoral level. I would like to express my sincere gratitude to Dr. Rehan Sadiq for his continuous support, enthusiasm, encouragement and patience throughout my Ph.D. at the University of British Columbia. I am grateful to him for the guidance, kindness, inspiration, and motivation that have made me resilient, diligent, and strong towards my academic and professional growth. Moreover, his overall insights have made this journey an inspiring experience. I would like to express my appreciation to Dr. Kasun Hewage for his support and advice. I would also like to thank my committee members, Dr. Shahria Alam and Dr. Zheng Liu. The advice and guidance I received from them have helped shape my initial research idea and enhance this thesis's quality. Besides, I would like to acknowledge the School of Engineering, UBC, and all their member’s staff for their continuous support during the past few years. The support and funds I received through the Natural Sciences and Engineering Research Council of Canada (NSERC), the Project Life Cycle Management Laboratory, data obtained from the Council of Canadian Fire Marshals and Fire Commissioners, and the Canadian Association of Fire Chiefs, and the research team have been enormous. I would like to express my special thanks to Dr. Amin Shotorbani, Dr. Bakhtavara Ezzeddin, Dr. Gyan Chhipi-Shrestha, Dr. Guangji Hu, Dr. Khondokar Mohammad Nahiduzzaman, and Dr. Yousefi Peyman for the support and collaboration. Moreover, my thanks go to all other current and past research team members. xix My deep and sincere gratitude to my adored family for their continuous and incomparable love, help and support throughout my life. I am deeply grateful to my mother Om Samir Chaabna and marhoom father Khalfi; my Lord, have mercy upon them, as they raised me up when I was little. I would like to express my heartfelt gratitude to my brothers Samir and his small family, Yazid, Fateh, and Nacereddine. My sincere gratitude is extended to my sisters Om Amira, Om Wiam, and their families. Finally, I would like to expand my sincere appreciation to all my professors and friends. xx DEDICATION Dedicated to my Beloved Family, Teachers and Friends. 1 CHAPTER 1: INTRODUCTION 1.1. Background The world has seen tremendous development in the building and construction industry in the last few years [1]. In Canada, building permits were estimated to reach an average value of $7.5 billion/month during 2013-18 [2]. In Canada, an average of 15,000 multi-unit residential buildings and 6,000 detached houses were built per month during April-August of 2018. Multi-unit residential buildings continue to cater to the housing needs of the growing Canadian urban population especially, due to several trends, such as declining household size, changing household characteristics, ageing population, and high prices of single-family homes in Canada [3]. Besides, the worldwide revenue of 58 billion dollars is expected to grow to 141 billion dollars by 2023 to integrate smart features to enhance the controlling and monitoring of buildings [4]. Furthermore, a transition to low-carbon, resilient economies could turn into investments of $23 trillion globally by 2030 [5]. This affects mainly buildings as they are found to be consuming more than 40% of overall energy and responsible for around one-third of greenhouse gas emissions [6]. Thus, smart and green features, such as foil insulation systems and smart power management, have been integrated into multi-unit residential buildings (SG-MURBs). Smart features are integrated for residents' comfort, whereas green features are integrated for efficient use of resources for environmental impact reduction [7]–[15]. The benefit of the features covers: (i) improving efficiency in buildings management, (ii) reducing the costs of energy consumption, and (iii) minimizing the overall impacts of buildings on human health and the natural environment. However, apart from the benefits, some smart and green features could have adverse impacts from a fire risk perspective [16]–[22]. Some features are associated with a high combustibility 2 rate for fire accidents, especially green features, such as petroleum-based insulation materials [23]–[25]. The integrated smart features for security, facilities management, and safety detectors help control fires; however, these features are electrical-based, causing fires in buildings [18]. Green buildings’ features, such as (i) insulation products, (ii) vegetative roofs, and (iii) high-performance glazing increases fire risk, which could endanger the residents’ life and the property [26]. Cladding, as a green feature, is found to be involved, as combustible material, with fires of high-rise buildings in several countries around the world, including Canada, USA, UK, UAE, and China [27]. Most of the insulations are foam-based products using petroleum derivatives, such as polyurethane and expanded polystyrene, that accelerates the destruction of the entire building. In addition, the amount of toxic smoke that could be produced by these insulations could present a major pollution hazard to the environment; besides, they make it difficult for firefighters to intervene. As well, dried plants are highly combustible materials for a fire [26][28][29]. Fire risk remains the main challenge, posing a significant threat to public safety, property, and the environment besides quality and sustainability [30][31]. Fire risk management is considered to be very important for SG-MURBs to enhance fire safety strategies and reduce fire risk to an acceptable level [32]–[35]. Several strides have been made towards risk management to minimize and control fire risk. These efforts include comprehensive fire risk assessment (FRA), enforcing building code regulations, promoting public education on fire hazards, and conducting evacuation and intervention training and drills [36]. Therefore, according to fire codes, such as of Canada [37] and Society of Fire Protection Engineers [38], and published literature [39]–[43], FRA, as an essential part of fire risk management, is required to determine the critical risk factors and enhance fire safety strategies. Besides, risk 3 assessment is an essential judging process for determining the significance of risk and the required risk reduction strategies [44]. Consequently, several models and methods for fire risk assessment were developed [33][45]–[50], including (i) FiRECAM was developed to help evaluate the risk to the life of occupants and building fire costs [51]. (ii) CESARE-Risk was developed to help identify possible cost-effective design solutions [52]. (iii) FIERAsystem was developed to help evaluate fire protection systems in industrial buildings [53][54]. (iv) CUrisk was developed to evaluate the overall fire risk of a building considering the available active and passive fire protection systems [55]. Furthermore, several approaches have been introduced to enhance fire safety, including the Bayesian belief net model to assess the risk of human fatality in fire incidents [56] and probabilistic methods to assess levels of people's safety in buildings [57]. A probabilistic risk assessment method is presented considering deterministic and stochastic factors for evacuation assessment [58]. A fire risk assessment method was presented by Ibrahim et al. [59], using a survey questionnaire for heritage buildings. Liu et al. proposed a fire risk assessment system for large-scale commercial buildings using the entropy weight method [59]. Xin and Huang used the scenario clustering technique to present a fire risk analysis model for residential buildings [60]. In FRA, both aleatory and epistemic uncertainties are the main challenges that should be well considered for an effective assessment [61][62]. Aleatory uncertainty originates from a lack of information and variability of data due to different sources' usage. Monte Carlo simulation is one of the common techniques used to handle this uncertainty by defining suitable probability distribution functions [63]. Whereas epistemic uncertainty originates from the lack of information, such as limited knowledge and imprecise evaluation of the essential processes of the modelled or real system's operation, e.g., expert judgment in linguistic scale having lower 4 and upper bounds [64][65]. The aleatory and epistemic uncertainties could manifest into three categories; (i) completeness, (ii) parameter, and (iii) model [66]–[72]. Completeness and parameter refer to a lack of comprehensive identification of the causal factors of fire events that should be well considered, including igniting objects, such as cooking equipment, heating equipment, electrical distribution equipment [73][74], and human error factors [75]. Whereas model uncertainties refer to the absence of involving all possible factors, their probabilities, and their influence on each other, leading to ineffective trade-off analysis of multi-scenarios and the multi-factors of fire incidence prediction [76]. Therefore, FRA's integrated models can help decision-makers understand the implications of investment decisions and actions serving as the foundation of regulatory decision-making to reduce the risk [77][78]. 1.2. Research Motivation The motivation of the research originates from the critical consequences of fire risk in SG-MURBs in Canada. A high number of fires that have occurred in SG-MURBs justifies the need for effective fire risk management. Countries worldwide, including developed and developing countries, record 3.8 million fires every year, caused 44,300 fire deaths [79]. More than 86.4 million fire incidents between 1993 and 2015 caused more than one million fire-related deaths and ~ USD 858 B loss [79]. The developed and developing countries report ~ 5,000 and 15,000 fire deaths every year, respectively [79]. In Spain, 77% of fatal fires occur in residential buildings [80]. In 2017, the USA reported 371,500 fires in the residential buildings that led to 2,695 deaths, more than 10,820 injuries, and USD 8B in economic loss [81]. According to the National Fire Information Database (NFID), Canada has recorded ~ 460,000 fires between 2005 and 2015, caused ~15,300 fatalities and CAD 7.3B loss. Moreover, 1,200 Canadian children were injured during 2006-13 due to residential fires [82]. Approximately 1.2 million 5 Canadians were directly affected by fires [32]. In 2017, the death rate from fires and burns reached 0.38, 1.16, and 0.88 per 100,000 individuals in Spain, the USA, and Canada, respectively [83]. British Columbia (Canada) reported ~ 24,000 fires in eight major cities, where 67% of these fires were related to residential buildings, 55% of these fires were associated with SG-MURBs causing severe consequences and 36% in single-detached houses. The remaining 9% of fire incidents occurred in other types of residential buildings, such as hotels [32]. Therefore, Canada's codes, including the BC Building Code, National Building Code of Canada, and Vancouver's Building Bylaw, require specific measures to help and support fire protection [84]. Moreover, there are very limited quantitative fire risk management models for SG-MURBs; decision-makers are obliged to assess buildings using qualitative-based checklists and fire safety plan guidelines. Therefore, developing a comprehensive and integrated framework helps provide effective fire risk management plans and tools with accurate results to reduce the relative frequency of fire risk and the adverse impacts on occupants, workers, properties, and the environment. Consequently, adequate safety of people, properties, and the environment would be ensured from prevention, protection, and intervention perspectives. Furthermore, the integrated framework would assist decision-makers, including policy-makers, regulators, managers, researchers, and society on assessing the critical fire risk factors and developing fire safety-based best management practices and guidelines. 1.3. Research Gaps This research originated based on the aforementioned challenges from the criticality of fire risk in SG-MURBs; the performed content analysis of the previous related studies found limited quantitative-based research for fire risk management in SG-MURBs [85][86]. Besides, 6 quantitative models and methods for fire risk management are limited [59][87]–[96]; decision-makers are obliged to assess buildings using qualitative-based checklists and fire safety plan guidelines [96][97]. Furthermore, the existing models and methods are mainly using conventional risk assessment methods, such as fault tree analysis, event tree analysis, and bowtie method, in developing limited fire scenarios [37][98]. These conventional methods are based on experts' knowledge and understanding in defining causes and consequences of fire accidents. Besides, the interactions among risk and safety events, their all possible states, nonlinearity among predictors and responses, the interdependence of risk factors, and the process of a large number of data are limited [90][94][100]–[103]. The available FRA-based fire safety approaches are mostly focused on fire resistance and available safe egress time [104]. Specific knowledge gaps on fire risk management are presented below based on a thorough review of the existing literature. 1. Investigating fire ignition sources in smart-green multi-unit residential buildings (SG-MURBs) is limited An integrated approach for assessing fire ignition sources is a challenge for SG-MURBs, incorporating uncertainties in the analysis, which can help prepare and improve fire prevention strategies accordingly [105][106]. Furthermore, limited models are available in the published literature to predict fire-related responses, including fire impacts, in SG-MURBs. Consequently, this study attempts to answer four critical questions to enhance ignition sources-based fire prevention in SG-MURBs: (i) What are the key potential ignition sources that induce fire incidents in SG-MURBs? 7 (ii) How to evaluate ignition sources quantitatively and determine the most critical factors? (iii) What are the most significant predictors for the consequence, the failure escape, and nature of casualties? How to predict the responses effectively and efficiently? 2. Information on the contribution of human error factors quantitatively in fire incidents of SG-MURBs is limited Published studies found that limited human error factors induced fire incidents (HEFs-FIs) have been considered as important factors in the existing studies [107][108]. To the best of the authors’ knowledge, investigating HEFs-FIs quantitatively in SG-MURBs is missing. Furthermore, a comprehensive review shows that no model is available in the published literature to predict HEFs-FIs [109]–[112]. Therefore, this study intends to answer four essential questions to enhance fire safety in SG-MURBs: (i) What are the potential human error factors that induce fire incidents? (ii) What are the most critical HEFs-FIs? (iii) How to assess HEFs-FIs effectively at different stages, such as cities and provinces, to help decision-makers develop the best fire safety practices? (iv) What are the most significant predictors of fire origin and ignition sources-based human factors? How to predict human error factors and the origin of a fire? 3. Need for an integrated fire protection approach to address combustible materials use in SG-MURBs According to fire codes, such as of Canada [37], Society of Fire Protection Engineers [38], and published literature [39]–[43], a combustible materials-based fire risk assessment (FRA) is 8 required to enhance fire protection strategies. Moreover, no model is developed yet to predict fire impacts, including damages of fire, the extension of fire, and dollar loss based on combustible materials and methods of fire protection to handle the related uncertainties in SG-MURBs. Concerning fire protection improvement in SG-MURBs, this study delves into answering four critical questions: (i) What are the potential combustible materials that ignite in fire incidents (CMs-FIs) in SG-MURBs? (ii) How to evaluate CMs-FIs quantitatively to determine the most critical factors? (iii) What are the most appropriate fire suppression materials (FSMs) and methods of fire control (MFC) for SG-MURBs? (iv) How to predict fire impacts based on CMs-FIs and MFC effectively and efficiently? 4. Selection and prioritization of effective fire protection and intervention strategies for SG-MURBs Several studies have investigated fire safety strategies [113]–[118]; however, investigating fire safety strategies quantitatively, handling the related uncertainties in SG-MURBs is challenging in the peer-reviewed literature. An assessment framework for safety strategies is required to investigate, predict, and optimize fire protection and intervention techniques to control the related potential impacts. Moreover, limited published models to predict fire-related impacts in SG-MURBs, which could help prepare and improve fire protection and intervention strategies accordingly. This study aims to develop an integrated approach to investigate and assess fire safety strategies; the approach attempts to address the following critical questions to enhance the fire safety strategies in SG-MURBs using ANN: 9 (i) What are the key potential factors of fire protection and intervention strategies for SG-MURBs? (ii) What are the most effective fire safety strategies to have very low fire impacts? (iii) How to predict fire impacts using fire protection and intervention strategies? (iv) What is the optimal combination of safety strategies that minimize fire impacts? 1.4. Objectives and Outcomes 1.4.1. Research objectives The main goal of this research is to develop an integrated fire risk management framework using machine learning algorithms to identify, analyze, evaluate, and prioritize fire risk and safety factors to enhance prevention, protection, and intervention strategies for SG-MURBs. To achieve this goal, specific objectives of the research are outlined as follows: 1) Perform a comprehensive content analysis to investigate state-of-the-practice for fire risk management in SG-MURBs; 2) Identify the potential factors that induce fire incidents in SG-MURBs and their mitigation techniques; 3) Develop benchmarks and machine learning-based models to predict and analyze fire risk in SG-MURBs and prioritize critical factors; 4) Assess the effectiveness of appropriate fire protection safeguards for combustible materials in SG-MURBs to rank and select the best protection measures; 5) Prioritize the most effective fire safety strategies and propose best management practices for SG-MURBs. 10 1.4.2. Research outcomes and deliverables The developed framework, using advanced machine learning algorithms, helps address the shortcomings in the existing approaches, especially related to handling aleatoric and epistemic uncertainties. The framework can analyze fire risk from different perspectives, including ignition sources, human errors, combustible materials, prevention techniques, protection measures, and intervention strategies for SG-MURBs. The developed benchmarks help improve fire risk management and select the critical factors of ignition sources, human errors, and combustible materials. Furthermore, the integrated framework will help in controlling the relative frequency of fire risk and its impacts, including loss of life and injuries, fire damages, fire extension, and dollar loss. Overall, this research helps identify, implement, and invest in the most appropriate fire safety strategies for continuous improvement. This research's deliverables help decision-makers improve fire prevention, protection, and intervention strategies; besides, they help decision-makers determine fire safety levels in SG-MURBs. Moreover, this research provides recommendations to improve fire codes and regulations, especially in the context of smart and green multi-unit residential buildings. 1.5. Metalanguage This section clarifies the specifically used terms in this study, in alphabetic order, to achieve the objectives mentioned above: Combustible materials: A combustible material is considered to be any form of material, including solid, liquid, and gas forms that could be ignited and burn under the conditions anticipated providing heat to an ambient fire. Extinguisher and suppression materials: An extinguisher is portable firefighting equipment that can be used manually to tackle fire in its initial stage to enhance the protection of both the 11 building structure and the occupancy [119]. The most common suppression materials are (i) Water, (ii) Foam, (iii) Dry powder using mono-ammonium phosphate, which is a dry chemical, (iv) CO2, which contain pure carbon dioxide, and (v) Wet Chemical uses a pressurized solution of alkali salts, the produced salts from the product of the neutralization of a strong base and a weak acid, in water. Fire risk assessment: Fire risk assessment (FRA) is a scientific approach to predict the occurrence of fire risk [71][120][121] to reduce fire risk impacts such as fire extension, fire damages, dollar losses, to an acceptable or ALARP level [71][122][123]. An integrated framework: An integrated framework for this study covers the investigation of related potential factors to enhance fire prevention, protection, and intervention strategies. Smart-green multi-unit residential buildings: Smart and green features have been integrated into multi-unit residential buildings (SG-MURBs), such as foil insulation systems and smart power management (heating, lighting, safety, and security), to enhance service quality for the comfort of residents and improve environmental sustainability with efficient resource use and reduced environmental impacts. Protection strategies: Fire protection strategies are the approaches that have been developed to enhance fire safety protection [124]–[126], including sprinklers as an automatic fire suppression system and extinguishers for manual firefighting [127]–[130]. Quantitative analysis: Quantitative analysis uses numerical methods for the precision of accident-likelihood and consequence-severity [131]. 12 Uncertainty in fire risk assessment: In fire risk assessment, aleatory and epistemic uncertainties are considered to be involved in FRA. Aleatory uncertainty originates from a random process that is due to the variation in the information from one source to another. Epistemic uncertainty originates from lack of knowledge, such as lack of understanding, less knowledge, or imprecise evaluation of the essential processes of the operation for the modelled or real system [64][65]. 1.6. Thesis Organization This thesis consists of eight chapters covering the literature review, methods used, developed models, results and discussions, key findings, and conclusions. Chapter 1: The first chapter presented an overall introduction covering the background and challenges, research gaps, research motivation, and research objectives and major deliverables. Chapter 2: This chapter summarizes the methodology of the thesis providing insight into the key research phases. The developed methodology to achieve the goal for each phase is presented; chapters 4-7 show detailed methodology for each phase. Chapter 3: This chapter presents a comprehensive literature review on the current state-of-the-practice for fire risk management, fire risk assessment methods for qualitative, semi-quantitative, or quantitative approaches in analyzing fire accident scenarios, and the related uncertainties in investigating for each phase of FRA. Chapter 4: This chapter developed an approach to support fire prevention through investigating the potential ISFs-FIs in SG-MURBs and analyze their relative frequencies with the related dollar loss. The approach covers establishing benchmarks for the relative 13 frequencies and the related dollar loss to evaluate ISFs-FIs. Besides, ANN and classification models were developed to predict fire impact. Chapter 5: This chapter developed a HEFs-FIs approach to identify, analyze, and evaluate human error factors, supporting fire prevention. The approach determines potential HEFs-FIs and analyzes their relative frequencies. Besides, evidential reasoning is used to aggregate HEFs-FIs for an effective assessment. Predicted ignition sources-related human factors and fire origin using ANN and classification. Chapter 6: This chapter developed an approach to assess combustible materials that can induce fire incidents (CMs-FIs) in SG-MURBs to enhance fire protection. The approach determines the appropriate fire suppression materials and methods of fire control accordingly. Furthermore, the approach developed an ANN model to predict potential impacts. Chapter 7: This chapter developed an approach to investigate essential fire strategies in SG-MURBs covering fire strategies assessment to support mainly fire protection and intervention. ANN models were developed to predict fire impacts and optimize the combination set of predictors that minimize fire impacts using genetic algorithms. Chapter 8: This chapter summarized and reflected on this research deriving conclusions from the study and recommendations. Moreover, the chapter covers originality, contributions, limitations, and future research. Figure 1-1 illustrates the connection among the achieved research objectives with the chapters' content; moreover, the main outcomes of the accomplished objectives are presented under the relevant chapters. 14 INTRODUCTIONBackgroundResearch gapMeta languageNFIDResearch motivationResearch objectives Thesis organisationLITERATURE REVIEWInvestigating state-of-the-practice for fire risk management Chapter:1Ignition source GroupsCOOKING EQUIPMENT EXPOSUREHEATING EQUIPMENT Combustible materialsGroupsBUILDING COMPONENTS FURNITURECLOTHINGPAPER PRODUCTS FLAMMABLE LIQUIDSFLAMMABLE GASES CHEMICALS, PLASTICSFORESTRY PRODUCTS ELECTRICAL EQUIPMENTGreenCombustible materialsCommonCombustible materialsSmartCombustible materialsAttributesINCENDIARY FIRESMISUSE OF SOURCE OF IGNITIONMISUSE OF MATERIAL IGNITEDMECHANICAL/ELECTRICAL FAILURE/MALFUNCTIONMISUSE OF EQUIPMENTHUMAN FAILINGHuman errorsChapter:3Common fire risk management practices Fire risk assessment approaches and methodsRelated challenges: uncertaintiesObjective 1IdentificationAnalysisPredictionObjective 2Objective 3Objective 4OptimizationObjective 5CONCLUSIONS AND RECOMMENDATIONSChapter:8Summary and conclusionsOriginality and contributionsLimitations of the studyFuture research AttributesELECTR DISTRIBUTION EQUIPMENT APPLIANCES AND EQUIPMENT SMOKER'S MATERIAL AND OPEN FLAME ELECTRICAL EQUIPMENT Chapter:5Chapter:7Chapter:6Investigation of ISFs-FIsAnalysis of HEFs-FIsAssessment of CMs-FIsFIRE SAFETY STRATEGIES AND BMPsRESEARCH METHODOLOGYChapter:2Identify the key potential ignition sourcesDefine the key factors of human error Identify the key potential combustible materialsDevelop benchmarks for ISF-FISDetermine the most critical ISFs-FIsDevelop benchmarks for CMs-FISDetermine the most critical CMs-FIsSelect the most significant predictors of ISFs-FIsDevelop a model for ISFs-FIs prediction and possible fire impactsSelect the most significant predictors of CMs-FIsDevelop a model to predict fire impacts using CMs-FIs and MFCDevelop benchmarks for HEFs-FISDetermine the most critical HEFs-FIsSelect the potential predictors of HEFs-FIsDevelop a prediction model for HEFs-FIsSelect potential factors of fire protection and intervention strategies Predict fire impacts using fire protection and intervention strategiesPropose the optimal combination of predictors that minimize fire impactsChapter:4GreenIgnition sourcesCommonIgnition sourcesSmartIgnition sources Figure 1-1: Synthesis of chapters and objectives 15 CHAPTER 2: RESEARCH METHODOLOGY The research methodology focuses on developing an integrated framework for fire risk management using machine learning to enhance fire prevention, protection, and intervention strategies. While the details of the proposed framework and the demonstration steps are provided under the relevant chapters, this chapter presents five phases to achieve the objectives of this study, as presented in Figure 2-1. 2.1. Phase I – Literature Review, Content Analysis, and Data Collection The first phase of this research investigated state-of-the-practice involving a comprehensive literature review that explores the common fire risk management practices to manage fire risk to people, properties, and the environment (chapter 3). This phase investigated the used methods for qualitative, semi-quantitative, and quantitative approaches in analyzing fire accident scenarios. Methods of fire risk assessment were investigated for hazard identification, risk analysis, and risk evaluation. Furthermore, the related uncertainties were investigated for fire risk assessment, including scenario completeness, parameter, and model uncertainties. The uncertainties were investigated for risk identification (hazard identification and likelihood identification), risk analysis (causes and consequences analysis), and risk evaluation. Fire incidents data for smart-green multi-unit residential buildings were collected and investigated for ignition sources, human errors, combustible materials, and fire safety strategies. The data collection is based on the existing literature and databases, including the NFID [1]. 2.2. Phase II – Potential Factor Identification and Mitigation Techniques This phase identified all possible fire risk and safety-related factors to identify the potential ones based on their relative frequencies and impacts, including fire damages, fire extension, content dollar loss, and building dollar loss. The phase identified the potential ignition source 16 factors-induced fire incidents (ISFs-FIs) in SG-MURBs, and the ignited combustible materials with the used safety strategies for prevention, protection, and intervention. This phase identified the potential ignition sources (common, smart, and green factors), including cooking, heating, and electrical distribution equipment (section 4.2.1). Phase II also identified human error factors that cause fire incidents (HEFs-FIs) to enhance prevention strategies, including misuse of equipment, incendiary fire, and misuse of source of ignition (section 5.2.1). Combustible materials were identified, such as building components, furniture, furnishings, and flammable liquids (section 6.2.1). Besides, this phase identified factors of the discovery of fire and actions taken (section 7.2.1). The most common green features also were identified from ignition and combustibility perspectives. Factors of fire protection features were identified as well, including manual fire protection facilities, sprinkler protection, fixed system other than sprinklers (supervised or watchman service, alarm to fire departments, and dry chemical system), fire detection devices, and outside fire protection. 2.3. Phase III – Fire Occurrence and Impact Analysis In this phase, relative frequencies and impacts of fire risk and safety factors were investigated and used in developing benchmarks to set the acceptability level and determine the dangerous factors that require immediate reaction for fire risk. The developed benchmarks prioritized critical fire risk factors in SG-MURBs, preventing, controlling, and mitigating undesirable events. The developed benchmarks help decision-makers define critical factors, including ignition sources, human errors, and combustible materials. Decision-makers use benchmarks to determine the factors' risk level, define critical factors, and compare the factors among cities. The benchmarks help cities install appropriate fire suppression materials and identify innovations that would improve fire safety strategies. 17 Phase I: Investigating state-of-the-practice for fire risk management Phase II: Potential factor identification and mitigation techniquesPhase III: Analysis of fire occurrence and impactsPhase IV: Prediction analysisPhase V: Fire response strategies and best management practices Common fire risk management practices in SG-MURBs Fire risk assessment approaches and methodsRelated challenges: uncertaintiesIgnition source factorsCombustible materialsDiscovery of fire and actions takenFire protection features Relative frequency of ignition sourcesFitting distributionBenchmarks for ISFs-FIs, HEFs-FIs, and CMs-FIsArtificial neural networkPerformanceRelative frequencyOptimization: fire response strategiesBest management practices Related impactsPrevention strategies improvementProtection strategies improvementIntervention strategies improvementDefines the scope and gap for the fire risk managementDefines the possible fire risk factors Identify the critical fire risk factorsIdentify the available mitigation techniques Benchmarks for factors to be assessedDetermine the critical factorsDevelop ANN modelsPredict possible fire scenariosPre-BMPsPro-BMPsInt-BMPsCFsGFsSFsCriticalityAcceptControlMitigateShareTransferNFIDSG-MURBs: smart and green multi-unit residential buildingsCFs: common featuresGFs: green featuresSFs: smart featuresISFs-FIs: ignition source factors that induced fire incidentsHEFs-FIs: human error factors that caused fire incidentsCMs-FIs: combustible materials that ignited in fire incidentsNFID: national fire information databasePre-BMPs: prevention best management practicesPro-BMPs: protection best management practicesInt-BMPs: Intervention best management practicesCFsGFsSFsCFsGFsSFsCFsGFsSFsCFsGFsSFsRegressionSimulationObjective 1Objective 2Objective 3Objective 4Objective 5 Figure 2-1: An integrated framework for fire risk management in SG-MURBs 18 2.4. Phase IV – Correlation and Prediction Analyses In this phase, correlation and association analysis were considered to select relationships among variables through p-value and a Spearman correlation coefficient. Classification and neural networks were used as the most suitable algorithms to predict potential fire impacts, as many inputs and outputs were involved, using several algorithms. Several parameters were considered to determine the performance of algorithms, including mean squared error (MSE), correlation coefficient (R), and determination coefficient (R2). ANN helped investigate a large amount of data set and detect complex nonlinear relationships among predictors and responses. Different types of fire impacts, including fire damages and dollar loss, were predicted to help decision-makers control the critical factors and invest in the appropriate safety strategies. 2.5. Phase V – Optimization-based Fire Response Strategies and Best Management Practices The optimization techniques, such as multiobjective-based genetic algorithms (section 7.2.3), were used as exceptionally effective techniques in providing a search strategy, generating possible solutions, and optimizing multidimensional impacts. The optimization helped to treat fire risk factors by selecting the best strategies, including accepting, controlling, mitigating, sharing, or transferring, considering relative frequency, related impacts, and criticality of each factor. 2.6. Framework Application The developed framework was applied in BC considering 23,324 fire incident scenarios for seven cities, including Kamloops, Kelowna, West Kelowna, Surrey, Vancouver, Vernon, and Victoria, with 2069, 1836, 257, 6469, 12003, 690, and 711 fire incident scenarios respectively. These interesting fire incident scenarios would enhance the estimation precision and draw 19 significant conclusions. Besides, the population for those cities was considered to investigate the relative frequencies of fire potential factors per capita. In this study, data quantity is varying based on the required investigation, including ignition sources, human error factors, and combustible materials. The required data of fire incidents in SG-MURBs are collected mainly from National Fire Information Database (NFID)[32][20]. NFID is a national data collection system for the occurred fire incidents from 2005 to 2015 in Canada. The NFID considers fire incidents' causes and their losses from provincial/territorial Fire Marshals and Fire Commissioners Offices across Canada. The NFID is considered one of the best fire incident data sources that could be used for such research projects as it links real fire incident data with other relevant socio-economic datasets. Detailed information of fire incidents, including (i) fire protection features, (ii) circumstances contributing to the outbreak of fire, (iii) factors relating to the origin and spread of fire, and (iv) fire loss details, can be found in NFID. Therefore, the NFID could be used as a reliable source for fire risk analysis and evaluation, making operational decisions to improve and generate policies, regulations to enhance prevention, protection, and intervention strategies. The seven cities were considered to show the applicability of the developed framework. The collected data from NFID were used for several purposes, including the relative frequency and dollar loss of risk factors were identifies and categorized; benchmarks were developed for risk factors; the appropriate suppression materials and methods of fire control were identified for the critical factors; the ANN models were developed, and optimization solutions were recommended. 20 CHAPTER 3: LITERATURE REVIEW A detailed review of fire risk management is presented in this section, focusing on fire risk assessment and risk treatment. Fire risk assessment covers fire risk identification, analysis, and evaluation of fire risk factors, including ignition sources and combustible materials in SG-MURBs. Fire risk treatment covers fire safety strategies for fire protection and intervention. Besides, challenges of FRA and source of uncertainties are investigated, exploring quantification of uncertainties. 3.1. Smart-Green Multi-Unit Residential Buildings In the last few years, SG-MURBs have seen several challenges, especially from energy, materials, and management perspective. Therefore, new features have been integrated, such as smart and green features for efficient use of resources [7]–[14]. Green features incorporate the best construction practices along with energy-efficient and materials into the construction of the building. Green features take into account fifteen main criteria in the different developed rating systems [133]–[138], including energy, CO2 emissions, ecology, economy, health and wellbeing, indoor environmental quality, innovation, land use, management, materials, pollution, renewable technologies, transport, waste, and water. Smart features ensure that different building systems communicate to minimize cost and enhance comfort besides environmental protection [139]–[141]. Due to the benefit of smart features in integrating technology advancement to respond to users' needs efficiently and minimize operations costs, the global smart building market is expected to grow from $5.73 billion in 2016 to $24.73 billion in 2021 [142]. The total investment in building construction has increased dramatically in the last few years. In Canada, the investment increased 5.0% to reach $16.0 billion in August 2020 [143]; the residential sector 21 has increased 8.2% to $10.8 billion. The investment for SG-MURBs was up 11.2%, whereas single-unit investment rose to 5.4% [143]. This investment supports the continuously increasing number of SG-MURBs to cater to the growing Canadian urban population [3]. In 2012, the construction of SG-MURBs exceeded single-family detached house construction; furthermore, more than 50% of the total planned residential developments are SG-MURBs [3]. The popularity of SG-MURBs is found to be due to several trends, including the expensive cost of single-family homes, declining household size, changing household characteristics, and an ageing population. 3.1.1. Construction materials Historically, installing non-combustible materials was the only way for constructing buildings to avoid large-scale conflagration [144]. In 2009, the British Columbia Building Code in Canada was revised to allow combustible material-based residential buildings to rise from four to six storeys [144][145]. This amendment requires integrating protection features, including sprinklers and extinguishers, to control and eliminate fires [144]. Furthermore, the National Building Code of Canada (NBC) classifies wood buildings as ‘combustible construction’ and permits the usage of combustible elements, including insulation, flooring, partition elements, electrical outlets, and wiring. The combustibility of materials is determined using the Canadian unlimited liability company's acceptance criteria, CAN/ULC-S114 [123]. Besides, buildings are categorized into five types of construction, based on the used materials, including fire-resistive, non-combustible, ordinary (Protected Combustible), heavy timber, and wood-framed [146]. In the last few years, wood is found to be used in all types of buildings, including schools, research facilities, and apartment buildings [145][147]. A protected wood frame that is commonly used in constructing newer apartment buildings has a 1-hour fire-resistance rating 22 for exterior walls, structural frame, floor, ceiling, and roof. However, the fire-resistance rating does not necessarily eliminate and control the movement of smoke and heat; thus, resulting in a massive fire impact on the level of safety and property damages [145]. Moreover, building content is found to play an essential role in controlling fire damages and extension. 3.1.1.1. A concrete foundation Concrete was patented at Portland cement in 1824 as the conventional way of constructing multifamily residential buildings. In 1854, cement was essential for building construction after the wood was around for centuries before concrete [147]. In 1904, cement was a material with flexibility, durability and strength used in Cincinnati, USA, to build the first high-rise building [147]. The concern of using cement and wood was augmented after a fire caught 241unit buildings with a wood-frame and burned to the ground in downtown Raleigh, N.C. USA [147]. However, lumber and forestry interests supported the usage of wood across North America enthusiastically [150]. Especially when fire protection strategies have been shown ability in helping control fires [144]. Regarding the environmental footprint, the manufacturing process of cement that is made up of ~ 70% aggregate and ~ 20% water requires energy that leads to pollution. Besides, considerable fuel to produce over 1,600 oC is needed for mining and processing steel impact carbon footprint [147]. 3.1.1.2. Wood-based construction Wood is considered an abundant and renewable natural resource that is suitable for many types of building construction, which could be residential and non-residential buildings, light frames or heavy timber frames, low rise or mid-rise. Wood is a multipurpose and often cost-effective building material. Therefore, it can be used in several building applications. Wood can be used to build roofs, ceilings, and walls. Wood structures are considered to be also strong, durable, 23 and resilient. Therefore, wood-based construction is supported by the government of Canada. Wood allows developers to have a building ready in a short time at a low cost; furthermore, wood-based construction can be designed to resist major earthquakes and hurricane-like winds [148]–[155]. In Canada, allowing and supporting wood-frame construction, in 2009, in the BC province led to building more than 300 mid-rise wood-framed buildings in ~ 10 years in the province [147]. The provinces, Quebec, Ontario and Alberta, have also followed the track to change their building regulations to allow construction of six-storey wood around 2013. In 2015, NBC permitted the usage of wood-frame construction for six-storey residential and commercial buildings [147]. Moreover, in Arlington, Va, the USA has also followed the trend, where it recently changed its building plans from concrete to wood-frame construction, declaring that 40% of the project's construction cost could be saved [147]. However, a study conducted for the Concrete Council of Canada by globe advisors found that the insurance premiums for wood-framed buildings were 6 times higher than similar concrete buildings [147]; this found to be due to several reasons, including having a high level of fire risk, the difficulties in attaining insurance for wood-frame structures, and low resistance to climate change, such as storms. Concerning the environmental footprint, wood has been found to have some issues to overcome in harvesting wood as it absorbs carbon dioxide [147]. 3.1.2. Smart features Smart features for SG-MURBs could be categorized into four main features (Figure 3.1), including smart security [156][157], smart facilities management [158][159], smart safety detectors[160]–[162], and smart infrastructure [158][162][163]. Smart security covers a 24/7 24 monitor system, access control, and video surveillance. Smart facilities management includes digital signage, digital alerts to spread safety news quickly and effectively, and HVAC control to monitor heating, ventilation, and air conditioning. Smart safety detectors and sensors involve smoke and heat detectors that send alerts to a smartphone to enhance fire prevention and protection. Smart infrastructure is based on (i) data network and cabling infrastructure as the physical connecting hardware and structured cabling for connectivity, communication, and operations purposes; (ii) smart evacuation process; and (iii) high-speed internet system for real-time monitoring. Figure 3-1 summarizes the identified smart features with specific systems that are found to be causing fires in SG-MURBs, defining related-relative frequencies. Smart featuresSmart Security Smart Facilities Management Smart Safety Smart InfrastructureMonitor 24/7Access ControlVideo Surveillance Digital SignageDigital AlertsHVAC ControlSmart LightingSmart Power Management Smart Proactive Maintenance of Equipment Smoke and Heat Detectors Smart Alarms Data Network and Cabling InfrastructureSmart Elevator High-Speed InternetCentral heating unit Water heater Permanent electric wiring, cable (includes power line, junction box) Copper conductors Panel board, switchboard (includes fuse, circuit breaker) Switch, outlet (includes receptacle, socket) Temporary electric wiring Appliance cord Motor, 1 HP & over (includes motor forming an integral part of an appliance or separated by a belt Motor under 1 HPIncandescent lamp, light bulb (includes lantern, flashlight) Fluorescent lamp (includes ballast) 1.74E-02/yr1.84E-03/yr4.31E-03/yr1.32E-02/yr1.84E-03/yr2.34E-03/yr4.15E-03/yr1.15E-02/yr2.98E-03/yr1.17E-03/yr1.89E-02/yr7.21E-03/yrInvestigationInvestigation Figure 3-1: Exploring smart features with the relative frequency of related ignition source systems 25 3.1.3. Green features Green features are integrated into SG-MURBs to reduce the overall impact on human health and the natural environment by (i) using energy, water, and material resources efficiently, (ii) protecting occupants’ health and (iii) reducing waste, pollution, and environmental degradation for the benefit of present and future generations. Third-parties around the world were established for verification of green features (Table 3-1), such as Canada Green Building Council (CaGBC), U.S. Green Building Council, Green Building Council of Australia (GBCA), and UK Green Building Council (UKGBC). Consequently, different rating systems are developed [133]–[135][137][138][165]–[170], as presented in Table 3-2, to assess and measure the greenness of buildings using several assessment criteria. The first rating system was developed in 1990 in the UK called BREEAM, which stands for Building Research Establishment Environmental Assessment Method. Then, LEED (Leadership in Energy and Environmental Design) was developed in the United States in 1998. Green building rating systems have been designed to assess the performance of the building throughout its entire lifespan. Table 3-1: Rating systems for green buildings in different countries around the world Rating system Meaning Country BEAM Building Environmental Assessment Method Hong Kong BERDE Building for Ecologically Responsible Design Excellence Philippines BREEAM Building Research Establishment Environmental Assessment Method UK CASBEE Comprehensive Assessment System for Built Environment Efficiency Japan DGNB German Sustainable Building Certificate Germany Greenship / Indonesia GBI Green Building Index Malaysia Green Star / Australia Homestar / New Zealand G-SEED Green Standard for Energy and Environmental Design Korea Green Star SA / South Africa Home Performance Index / Ireland LEED Leadership in Energy and Environmental Design U.S., Canada, Brazil NABERSNZ National Australian Built Environment Rating System Australia SGBP/SC Singapore Green Building Product/Services Certification Singapore CGBS “Three Star System” China green building system China 26 Table 3-2: Comparison of the Assessment criteria between the most used rating systems Assessment criteria Rating systems with references BREEAM BEAM CASBEE DGNB GREEN STAR LEED BERDE CGBS Greenship GBI G-SEED Green Star SA Ref [171] [172] [171] [170][173] [174] [171] [175] [176] [177] [178] [172] [179] [179] [180] Energy √ √ √ √ √ √ √ √ √ √ √ √ CO2 √ - - √ - √ √ - - - - √ Ecology √ √ √ √ √ √ √ - √ - √ √ Economy - - - √ - - - - - - - - Health and wellbeing √ √ √ √ √ - - - √ - - - Indoor environmental quality √ √ √ √ √ √ √ √ √ √ - √ Innovation √ √ - - √ √ - - - √ - - Land use √ √ - √ √ √ √ √ √ √ √ √ Management √ √ √ √ √ - √ √ √ √ √ √ Materials √ √ √ √ √ - √ √ √ √ √ √ Pollution √ √ √ √ √ √ - √ - - √ - Renewable technologies √ - - √ √ √ - - - - - - Transport √ √ - √ √ √ √ - - - √ √ Waste √ √ - √ - - √ - - - - - Water √ √ √ √ √ √ √ √ √ √ √ √ Fire - - - - - - - - - - - - 27 Fifteen criteria are mainly used in the different rating systems, [133]–[138] as presented in Table 3-2, including energy, CO2 emissions, and indoor environmental quality. According to the World Green Building Council (WGBC) [181], by the end of 2017, Canada has recorded 1 billion square feet of LEED-registered space in its land; New Zealand built 20000 green homes; over 2000 commercial buildings were registered with LEED certification in Brazil; India registered in 2017, 500 new green building; Germany recorded a green building growth of 30% from 2016 to 2017. In the USA, more than 92,000 commercial projects registered and certified in LEED, equivalent to more than 1.8 billion square meters; and South Africa reached the 350th green building certification. While green features are developed for a positive impact, some features are found to have a negative impact from a fire perspective, including insulation products, vegetative roofs, and solar panels [20][177][182]; table 3-3 shows the related potential hazards to different green criteria [26]. Insulations are foam-based products using petroleum derivatives, such as polyurethane and expanded polystyrene, that accelerate the destruction of the entire building. Moreover, different materials were derived from petroleum to be used for buildings, such as plastics, laminated countertops, adhesives for plywood and particleboard, insulation, carpeting, and paints [183]. Besides, the large amount of toxic smoke produced by these insulations presents a major pollution hazard to the environment. This may also make it impossible for firefighters to intervene. Volatile oil “essential oil” based plants, including tea, clove, lavender, and fragrance, could help fires to spread, and dried plants are good combustible material for a fire [184]. Besides, Photovoltaic (PV) systems cause fire risk with catastrophic consequences (Table 3-4) [20][185]–[187]. Furthermore, PV-induced fire incidents increase linearly to reach 28 36% in 2018, as presented in Figure 3-2 [188]. Therefore, a comprehensive fire risk management of green features is required. Table 3-3: Potential hazards of green building features: Low (L), Medium (M), High (H) Potential hazards Green building features Poses potential ignition hazard Poses potential shock hazard Poses potential explosion hazard Poses potential toxicity hazard Readily ignitable Burns readily once ignited Contributes more fuel / increased heat release rate Material affects burning characteristics Fast(er) fire growth rate Significant smoke production/hazard Potential for shorter time to failure Failure affects burning characteristics Failure presents stability concerns Failure presents smoke spread concern Failure presents flame spread concern Material presents flame spread concern May impact smoke/heat venting May impact occupant evacuation May impact fire-fighter (FF) water availability May impact suppression effectiveness May impact fire apparatus access May impact fire-fighter (FF) access and operations May impact containment of runoff Relative risk level Structural Materials and Systems Fibre-reinforced polymer (FRP) elements L L L L M H H L L L H L H L H H L M L L L H L H Lightweight concrete L L L L L L L L L L L L L L H L L M L L L M L M Lightweight engineered lumber L L L L M H H L L L H L H L H H L M L L L H L H Plastic lumber L L L L M H H L L L H L H L H H L M L L L H L H Bamboo L L L L M H H L L L H L H L H H L M L L L H L H Bio-polymer lumber L L L L M H H L L L H L H L H H L M L L L H L H Extended solar roof panels L L L L L L L L L L L L H L L L L L L H L H L H Exterior Materials and Systems Structural integrated panel (SIP) L L L L M M H H M H L H L L L H L L L M L L L H Rigid foam insulation L L H H H H H H H H L H L L L H L M L M L L L H Exterior insulation & finish (EFIS) L L L L M M H H M H L H L L L H L L L M L L L H Spray-applied foam insulation L L H H H H H H H H L H L L L H L M L M L L L H Foil insulation systems L M L L M M M H H H L H L L L H L M L M L L L H High-performance glazing L L L L L L L M L L L M M M M L H M L M L H L M Low-emissivity & reflective coating L L L L L L L M L L L M M M M L H M L M L H L M Bamboo, other cellulosic L L L L L L L L L M L L L L L H L L L L L L L M Double-skin façade / cavity walls L L L L L L L M L L L M H H H L H M L M L H L M Bio-polymers, FRPs L L L L L L L L L M L L L L L H L L L L L L L M Vegetative roof systems L L L L M M M L L L L L L L L M L L L L L H L M Exterior cable / cable trays L L L L L L L L L M L L L L L H L L L L L L L M Façade Attributes Area of glazing L L L L L L L L L L L L L L L L M M L L L M L L Area of combustible material L L L L L L M M L M L L L L L L M M L L L L L L Exterior vegetative covering L L L L L M M L L L L L L L L M L H L M M M L M Interior Materials and Finishes FRP walls/finishes L L L M L M H M M H L L L M M H L M L M L L L M Bio-polymer wall/finishes L L L L L M H M M M L L L M M H L M L M L L L M Glass walls L L L L L L L L L L L L L M M M L M L M L L L M Bio-filtration walls L L L L L M M M M M L L L L L M L M L M L L L M Wood panel walls/finishes L L L L L L L L L L L L L L L L L L L L L L L L Bamboo walls/finishes L L L L L M H M M M L L L M M H L M L M L L L M FRP flooring L L L L L M H M M H L L L L L H L L L M L L L M Bio-polymer flooring L L L L L M H M M M L L L L L H L L L M L L L M Bamboo flooring L L L L L M H M M M L L L L L H L L L M L L L M Interior Space Attributes More enclosed spaces L L L M L L L L M L L L L L L L L L L L L L L M Higher insulation values L L L L L L L L M L L L L L L L L L L L L L L M Tighter construction L L L M L L L L L L L L L L L L L L L L L L L M More open space (horizontal) L L L L L L L L L L L L L M L L L M L L L L L M 29 Interior vegetation L L L L L L L L L L L L L L L L L L L L L L L L More open space (vertical) L L L L L L L L L L L L L M L L L M L L L L L M Building Systems & Issues Natural ventilation L L L L L L L L L L L L L M L L L M L L L M L M High volume low-speed fans L L L L L L L L L L L L L L L L L L L M L M L M Refrigerant materials L L M M L L L L L L L L L L L L L L L L L M L M Grey-water for suppression L L L L L L L L L L L L L L L L L L M M L L L M On-site waste treatment L L L M L L L L L L L L L L L L L L L L L L L L On-site water treatment L L L M L L L L L L L L L L L L L L L L L L L L On-site cogeneration L L L M L L L L L L L L L L L L L L L L L L L L Rain-water for suppression L L L L L L L L L L L L L L L L L L M M L L L M Reduced water suppression systems L L L L L L L L L L L L L L L L L L M M L M L M Alternative Energy Systems Tankless water heaters M L L L L L L L L L L L L L L L L L L L L L L L Wood pellet systems L L L L L H H L L L L L L L L L L L L L L L L M Electric vehicle charging station L L L L L L L L L L L L L L L L L L L L L L L L Cogeneration systems L L L L L L H L L L L L L L L L L L L L L L L M Battery storage systems M M L L L L M L L L L L L L L L L L L L L L L M Hydrogen fuel cells L L L L L H H L L L L L L L L L L L L L L L L H Wind turbines M L M L L L L L L L L L L L L L L L L L L H L M Oil-filled PV panels L L L L L L H L L L L L L L L L L L L L L H L H PV roof panels H H L L L H L L L L L L L L L L L L L L L H L H Site Issues Hydrogen infrastructure L L M L L L L L L L L L L L L L L L L L L L L L Community charging stations M M L L L L L L L L L L L L L L L L L L L L L L Reduced water supply L L L L L L L L L L L L L L L L L L M M L M L M Localized waste treatment L L L L L L L L L L L L L L L L L L L L L M L M Localized water treatment L L L L L L L L L L L L L L L L L L L L L M L M Localized energy production L L L L L L L L L L L L L L L L L L L L L M L M Increased building density L L L L L L L L L L L L L L L L L M L L M L L M Building orientation L L L L L L L L L L L L L L L L L M L L M L L M Vegetation for shading L L L L L L L L L L L L L L L L L M L L M L L M Water catchment/features L L L L L L L L L L L L L L L L L L L L L L M M Extent (area) of lawn L L L L L L L L L L L L L L L L L L L L M L M M Use of pavers L L L L L L L L L L L L L L L L L L L L H L M H Permeable asphalt paving L L L L L L L L L L L L L L L L L L L L H L M H Permeable concrete systems L L L L L L L L L L L L L L L L L L L L H L M H 0.0%10.0%20.0%30.0%40.0%50.0%60.0%2015 2016 2017 2018PercentYear Figure 3-2: Fire incidents in the USA due to photovoltaic (PV) systems 30 Green building feature Consequences Type of building Location Year Ref Roof PV system The roof is completely damaged; damages extended to the first and ground floors. Residential Winchester, UK Jun 2018 [189] Roof PV system - Commercial Mount Holly City in North Carolina, USA April 2011 [20] PV system - Residential San Diego, CA, USA April 2010 [20] Roof PV system The roof of a house is destroyed. Also, a man was hospitalized. Residential Hampshire, UK Jun 2018 [190] Roof PV system - Commercial Bakersfield, CA, USA April 2009 [20] PV fire - Residential Sittingbourne, UK March 2012 [20] Roof PV system - Commercial Goch city in Germany April 2012 [20] Foam insulation - Home North Falmouth, MA, USA May 2008 [20] Three fires with foam insulation Destroyed a $5 million home Home Woods Hole, MA, USA February 2011 [191] Foam insulation A house is almost burned to the ground House Montreal, Quebec, Canada May 2010 [192] Exterior façade 17 people were injured in the fire. The fire consumed a foam-like building material affecting five floors Hotel LAS VEGAS January 2008 [193] Sandwich Panels, aluminum cladding on the exterior - Hotel Atlantic City, New Jersey, USA September 2007 [194] Exterior façade 4 people hospitalized. The fire spread across 33 floors in less than 30 minutes. Apartments Busan, Korea October 2010 [195] Exterior façade 58 people died and 70 more were injured. The flames spread quickly to the entire structure. 30-story apartment building Shanghai, China November 2010 [196] Exterior façade The fire left 125 families homeless. Residential Tower Sharjah, UAE April 2012 [197] Exterior façade One firefighter was killed and seven people were injured. The fire had spread from the lower floors to the building’s crown. Hotel Beijing, China February 2009 [198] Table 3-4: Related fire incidents with green features around the world 31 3.2. Fire Risk Management Fire risk management is vital in the prevention, protection, and intervention of fire incidents [199], focusing on two essential phases, fire risk assessment and fire risk treatment. Figure 3-3 depicts the main steps of fire risk management with some of the methods used for each risk assessment step. Fire risk assessment (FRA) is a process of identifying, analyzing, predicting, and evaluating fire risk to the life of people, property, and the environment [36]. FRA is a scientific approach developed to predict and reduce the occurrence of fire risk [71][120][121]. Table 3-5 presents several studies for FRA using three main approaches: qualitative, semi-quantitative, and quantitative. Quantitative fire risk assessment (QFRA) is considered to be the best approach due to its precision of the magnitude of potential loss and its probability [131][200][201]. The selection of the approach depends on the purpose of fire risk analysis, the type of risk, and the availability of information resources [36]. The primary purpose of QFRA is to reduce the risk to an acceptable level [71][122][123]. Due to the severity of fire accidents that influenced people, properties, and the environment, QFRA becomes very important for decision-makers to reduce fire risk to a tolerable level. Besides, QFRA is challenging several uncertainties, including completeness, parameters, and modelling, as the presented details in the section 3.3 and Figure 3-3. 32 Risk identificationRisk AnalysisIdentify potential hazards and their nature Hazards identification processWhat-If techniqueHazards identification techniquesHAZIDFire risk factors Fire scenariosRisk identification processData-based referencesRisk identification techniquesCause and effect diagramRelative frequency analysis Probability failure on demand (PDF)Likelihood analysisFTA ETARisk analysis techniquesBow-tie analysis HRAFire risk impact PropertiesConsequence analysisEnvironmentIs risk acceptable?Fire risk acceptanceYesMore investigations are requiredFire risk assessment objective and acceptability criteriaInternal and external stakeholders inputs; managers, occupants, experts, and policy-makersRefine analysisCheck risk acceptability thresholdIntegrate appropriate safeguards for the critical eventsNoFire risk management; Decision making, policy, risk reduction actionsRelated uncertaintiesCompleteness Parameters ModellingRisk Identification Uncertainties InRisk Analysis Uncertainties InRisk Evaluation Uncertainties InFire risk contributors Vagueness in relative frequency Interaction between fire factorsSelecting fire risk factors Real-time data for fire risk factors Complexity phenomenaInterpretation of resultsFire risk and safety results not based on real-time data Inadequacy in selecting fire risk factors and their safety measures PeopleScreening of fire hazardsSelecting fire risk impacts Selecting fire safety barriersInadequacy of fire risk causes and consequences modelling `Inadequacy of acceptance criteria Note: HAZID: hazard identification, FTA: fault tree analysis, ETA: event tree analysis, HRA: human reliability analysis. Figure 3-3: Fire risk management framework with the related uncertainties 33 Table 3-5: Related studies on buildings fire protection Authors Year Objectives/Strength Applications Limitations/Suggestions Ref Nishino Tomoaki 2019 Developed urban fire spread simulation with the stochastic occurrence of spot fires Residential buildings Investigating the urban fire spread simulation enhances and supports firefighting to control fires [202] Gritzo et al. 2011 Analyzed the contribution of fire risk on the total lifecycle carbon emission and fire impacts Residential buildings Managing fire risk is essential in achieving sustainability and avoiding fire impacts [203] Farid et al. 2019 Developed EvacuSafe tool to enhance evacuation safety performance. Residential and commercial buildings Using specific indices, such as Route Risk Index and Compartment Risk Index to support safety evaluation performance [204] Wong and Fong 2010 Presented a method to determine the probable risk to evacuees taking into consideration exit design, occupant load and its variations Buildings Occupant-load ratio, total exit width and flow rate significantly affects risk assessment [205] Lui and Tong 2010 Identified the major inadequacies in the fire safety aspects Residential Care Home for the Elderly Further improvement should be implemented on fire risk factors and fire safety management [206] Vigil and Trueba 2019 Developed a methodology to manage fire incidents using extracted information from the media Elderly Home Exploring fire data collection and analysis are essential for efficient fire safety measures [207] Jennings Charles 2013 Reviewed residential fire risk considering social, economic, and building stock characteristics Urban neighbourhoods Discovering a high number of case studies helps to identify potential variables for a quantitative approach [208] Xiong et al. 2017 Investigated 215 unintentional residential fires using odds ratio analyses Residential buildings Investigation human errors, including impairments, enhances fire prevention strategies [209] Lau et al. 2015 Proposed a fire risk scorecard to support fire risk assessment Residential buildings Machine learning algorithms, such as SVM, could support fire risk assessment [210] Xin and Huang 2014 Analyzed the fire situation in China, investigating fire characteristics and the elements affecting fire risks Residential buildings Integrating statistics extracts useful information from the actual events, helping to develop proactive fire protection measures [211] Xin and Huang 2013 Presented fire risk analysis model using scenario clusters to support decision making Residential buildings Measuring fire risk improves building fire safety and reduce fire damage [86] Mi et al. 2020 Presented a model to deal with the uncertainty using fuzzy theory and evidence reasoning theory Residential building Determining fire risk levels supports fire risk assessment [212] Hansen et al. 2018 Developed risk-based decision-support tool to evaluate the impacts of fire risk on the occupants and the property Residential high-rise buildings Exploring detailed statistical data for fire scenarios helps to introduce efficient barriers and reduces the risk level [213] Farid et al. 2017 Developed a fire risk indexing methodology to define the presence of ignition and fuel sources High-rise residential buildings Investigating fire risk incidents enhances awareness among occupants and improves fire safety performance [214] Austin et al. 2018 Presented a method to estimate fire damage in homes Residential building Fire risk quantification leads to well-diagnosing fire problem and enhances safety strategies [215] 34 3.2.1. Fire risk identification Fire risk identification as the first step in the FRA process identifies the initiating fire risk events, possible outcomes, safeguards and the general information related to the fire risk in SG-MURBs. The identification of this step includes, ignition sources [74][216]–[220]; combustible materials [221]–[223], act or omission [224]–[226], area-origin of fire [227]–[229], spread of fire [230]–[232], and initial detection techniques [233]–[237]. Figure 3-4 identifies and categorizes the potential ignition sources, combustible materials, and discovery of fire and action taken for SG-MURBs. Ignition sources include cooking, heating, and electrical distribution equipment; it also identifies human error factors that cause a fire (HEFs-FIs) to enhance prevention strategies, including misuse of equipment, incendiary fire, and misuse the source of ignition. The figure summarises the potential factors from different perspectives that would help reduce fire impacts; better-identifying factors related to fire risk the better control of fire impacts, including damages and victims factors. Figure 3-4: Potential risk and safety factors identification with possible fire impacts 35 This step identifies combustible materials, such as building components, furniture, furnishings, and flammable liquids. Furthermore, the step identifies factors of the discovery of fire and actions taken, factors of fire protection features, including manual fire protection facilities, sprinkler protection, fire detection devices, and outside fire protection. Note: All types of residential buildings include town housing, condominium, apartment, tenement, hotel, motel, single/semi-detached, n-plex, educational institution, and mobile home/trailer park. Figure 3-5: Top 50 cities with total fire incidents in all types of residential buildings and SG-MURBs between 2005 and 2015 The identification is based on the existing literature and fire databases, including the National Fire Information Database (NFID) [32]. According to the NFID, figure 3-5 presents the top 50 0 2000 4000 6000EDMONTONCALGARYHAMILTONLONDONABBOTSFORDBRAMPTONWINNIPEGLANGLEYKITCHENERBRANTFORDKINGSTONKAMLOOPSVANCOUVERBURLINGTONCHILLIWACKMISSISSAUGACLARINGTONCOQUITLAMOTTAWAMILTONCAMBRIDGEKELOWNASURREYMARKHAMCHATHAMBRANDONNANAIMOTORONTOBURNABYAJAXCFB SUFFIELDCORNWALLGRANDE PRAIRIERICHMONDBARRIEDELTATHUNDER BAYGRANDE PRAIRIEBELLEVILLECALEDONFLAMBOROUGHLINCOLNINNISFILPRINCE GEORGEMEDICINE HATFORT MCMURRAYFIRE INCIDENTSCANADIAN CITIESALL TYPES OF RESIDENTIAL BUILDINGS05001000WINNIPEGVANCOUVEREDMONTONMISSISSAUGACALGARYOTTAWALONDONSURREYTORONTONANAIMOABBOTSFORDRICHMONDTHUNDER BAYBRAMPTONHAMILTONPRINCE GEORGEKITCHENERKAMLOOPSLANGLEY TOWNSHIPWINDSORST. CATHARINESKINGSTONVAUGHANOAKVILLEMILTONSAANICHBRANTFORDSUDBURYBURLINGTONTHOMPSONSASKATOONSAULT STE MARIECLARINGTONOSHAWACITY OF CHILLIWACKNIAGARA FALLSRED DEERMARKHAMWELLANDNEW WESTMINSTERWHITBYSARNIASTONEY CREEKPENTICTONCAMBRIDGERICHMOND HILLKELOWNAFIRE INCIDENTSCANADIAN CITIESSG-MURBs 36 cities with total fire incidents, considering all types of residential buildings and SG-MURBs, between 2005 and 2015. 3.2.1.1. Ignition source factors Exploration of the source of ignition is necessary to identify the potential ignition sources in the buildings [38] and helps predict the relative frequency of fire risk as well as proposing effective prevention techniques [238][239]. Investigation of ignition sources helps enhance safety strategies and control a fire in less time [34]. Furthermore, identifying the potential ignition sources improves systems' reliability to continue operating safely and minimizes their relative frequencies [38]. Besides, investigating ignition frequency is essential for quantitative estimation [240]. Annual ignition frequency is determined using the national fire database as the main derivative source of ignition frequency for all possible ignition source categories [240]; SG-MURBs are found to have numerous fire ignition sources [230]. Ignition sources can cover several categories as presented in Figure 3-6 [38], including cooking equipment [240]–[242], heating equipment [243][244], appliances and equipment [245]–[247], electrical distribution equipment [248]–[252], smokers materials and open flame [253], and exposure [254]–[256]. The potential ignition source factors in SG-MURBs are reviewed [32][33]; Figure 3-6 shows a comparison percentages of the ignition source categories between all types of residential buildings and SG-MURBs in Canada. All types of residential buildings include town housing, condominium, apartment, tenement, hotel, motel, single/semidetached, n-plex, educational institution, and mobile home/trailer park. Cooking equipment and smoker materials are the top ignition source categories in the Canadian SG-MURBs and residential buildings, respectively. Detailed ignition source categories are presented in Figure 3-7, where smokers’ materials cover several factors, such as cigarettes and matches [257]; naked flames 37 cover candles and night lights, boilers, cookers, and toasters; faulty or misused electrical equipment cover electric blankets, televisions, computers, washing machines and dryers. Note: All types of residential buildings include town housing, condominium, apartment, tenement, hotel, motel, single/semi-detached, n-plex, educational institution, and mobile home/trailer park. 3.2.1.2. Human error factors Human error factors were the primary causes of fire incidents; thus, several studies investigated the relationship between human factors and fire risks [258]–[260]. Besides, limited studies have been conducted focusing on the assessment of HEFs-FIs in SG-MURBs. According to NFID, roughly 58.5% of fire incidents in SG-MURBs of seven cities in BC Canada were caused by human errors [32]. Therefore, assessing human error factors that cause fire incidents (HEFs-FIs) is a significant fire risk prevention task. Figure 3-7 summarizes the relevant HEFs-FIs to fire incidents in SG-MURBs, considering several classes, including incendiary fires, misuse of ignition sources, and misuse of material ignited [32][33]. The figure depicts a comparison between human error factors for residential buildings and SG-MURBs. 29.4%10.3%5.3%8.8%3.9%28.4%8.6%5.3%42.0%2.5%4.4%5.5%3.1%35.1%2.3%5.0%Cooking equipmentHeating equipmentAppliances and equipmentElectrical distribution equipmentOther electrical equipmentSmoker’s material and open flameExposureMiscellaneousIgnition sources categoriesSG-MURBs All types of residential buildingsFigure 3-6: Percentages of ignition sources categories in all types of residential buildings and SG-MURBs in Canada between 2005 and 2015. 38 Figure 3-7: Fire ignition sources in smart and green multi-unit residential buildings 39 Figure 3-8 depicts that misuse of equipment is the top human error category in causing fires in the Canadian SG-MURBs. In contrast, human failing is the top category of human error in inducing fire incidents in all types of residential buildings. Note: All types of residential buildings include town housing, condominium, apartment, tenement, hotel, motel, single/semi-detached, n-plex, educational institution, and mobile home/trailer park. Table 3-6 presents a related studies summary of human error factors that induced fire incidents. The table covers the study scope, nature study, building type and investigated HEFs-FIs. Table 3-7 depicts several categories and detailed factors of HEFs-FIs that contribute significantly to fire incidents. In Canada, smokers’ materials are the primary cause of fire-related death, leading to an average of more than 70 deaths per year; smokers’ materials cause more mortalities and property damage than other ignition sources [108]. 17.4%12.2%12.3%14.7%3.1%10.0%21.2%0.1%8.9%16.1%14.0%14.8%9.1%1.8%18.9%16.2%0.0%9.1%Incendiary firesMisuse of source of ignitionMisuse of material ignitedMechanical/electrical failure/malfunctionConstruction, design or installation deficiencyMisuse of equipmentHuman failingVehicle accidentMiscellaneousCategories of human error factorsSG-MURBs All types of residential buildingsFigure 3-8: Percentages of HEFs-FIs for all types of residential buildings and SG-MURBs in Canada between 2005 and 2015 40 Table 3-6: A summary of human error factors induced fire incidents Fire investigation Study scope Occupants during a fire Human behaviour in fire incidents Human response to non-injury accidents Human behaviour Fatal fires Human behaviour in fire incidents Human behaviour in fire incidents Nature of study Literature review Interview and questionnaire survey Potential interviewees Literature review Statistical report Literature review Literature review Building type High-rise buildings Multi-storey residential buildings in the UK House fires in Australia Dwelling fires in the UK Residential buildings in the USA High-rise residential building in the UK Buildings Human error factors Incendiary fires-children - - √ - - - - Riot, civil disturbance - - - √ - - - Vandalism, mischief - - - - √ - - Smokers' material - - √ √ √ - √ Inadequate control of "open" fire - √ √ √ √ - √ Playing with a source of ignition-child - - √ - √ - √ Welding or cutting close to flammable materials - - - - - - - A torch is close to flammable materials - - √ - - - √ Flammable liquid to kindle fire - - - - - - - Improper container √ - - √ - - √ Overheated cooking oil, grease, wax - - √ - √ - - Combustibles are placed close to heat - - - √ - - - Improper storage √ √ - √ - - - Lack of maintenance √ √ √ √ √ - √ Equipment unattended √ - - - √ - - Asleep (excludes suspected use of alcohol, drugs or medication) - - - - - - - Temporary loss of judgement suspected (includes panic) √ √ - √ - √ √ Physical or mental disability √ - √ √ - - √ Suspected impairment - use of alcohol, drugs or medication - - - - - - - Asleep - suspected use of alcohol, drugs or medication - - - - - - - Automobile - - - - - - - Rekindle - - - - - - - References [261] [92] [262] [263] [264] [265] [226] 41 In the USA, fires due to smokers’ materials increase by 19 % in the most recent years and cause fatal fires. In Denmark, smokers’ materials are the most common cause of residential fire deaths; and in the UK, smokers’ materials are the cause of almost 50% of fire deaths in dwellings [108]. Table 3-7: Potential human error factors in SG-MURBs Human error factors References INCENDIARY FIRES Incendiary fire [32][266] Suspicious Riot, civil disturbance Vandalism, mischief MISUSE OF SOURCE OF IGNITION Smokers' material [32][108][252][267] Thawing Inadequate control of "open" fire Torch too close MISUSE OF MATERIAL IGNITED Flammable liquid to kindle fire [32][38][119][243][268][269] Improper container Overheated cooking oil, grease, wax Combustible placed too close to heat Improper storage MECHANICAL/ELECTRICAL FAILURE/MALFUNCTION Part failure, leak, break [32][218][232][243][245] [252][270]–[275] Electrical short circuit Part worn out Mechanical/electrical failure/malfunction Design deficiency Construction deficiency MISUSE OF EQUIPMENT Lack of maintenance [108][249][260][274][276] [277] Equipment unattended HUMAN FAILING Asleep (excludes suspected use of alcohol, drugs or medication) [32][108][209][226][232] [257][273][278]–[280] Temporary loss of judgement suspected (includes panic) Physical or mental disability Accident Suspected impairment - use of alcohol, drugs or medication Asleep - suspected use of alcohol, drugs or medication Distracted, preoccupied 42 3.2.1.3. Combustible materials Investigating combustible materials supports decision-makers in enhancing fire safety strategies to reduce the impact of fires effectually. In the UK, building regulations were updated in 2018 for England to control buildings' combustibility materials, including residential blocks of flats [281]. Exploring combustible material is essential in controlling fire hazards, improving combustibles and flammables' storage procedures, controlling ignition sources, and installing active fire protection systems [98][239]. Moreover, considering the combustibility of materials supports the provision of fire safety and developing a safe code for prevention, protection, and intervention [145]. Different fire protection strategies could be developed and installed accordingly [124]–[126], including (i) passive fire protection (fire-resistant walls, floors, and doors), (ii) sprinklers as an automatic fire suppression system, (iii) fire brigades, and (iv) extinguishers for manual firefighting [127]–[130]. Discovering and classifying the potential combustible materials helps in selecting appropriate suppression materials, including Water, Foam, Dry powder, CO2, and Wet Chemical, for SG-MURBs [252][282]–[284]. The combustible materials could be classified into six main fire classes: ▪ Class fire “A” is based on flammable solids, such as wood, paper, and fabric. ▪ Class fire “B” is based on flammable liquids, such as petrol and paint. ▪ Class fire “C” is based on flammable gases, such as hydrogen, butane, and methane. ▪ Class fire “D” is based on combustible metals, such as magnesium and aluminum. ▪ Class fire “E” Electrical fire is based on electrical equipment, and ▪ Class fire “F” is based on cooking oils, typically a chip-pan fire. Eight primary potential combustible materials were ignited in the Canadian SG-MURBs as the details presented in figure 3-9 compared with the ignited combustible materials in all types of 43 residential buildings between 2005 and 2015. The potential combustible materials cover building components, furniture, clothing-textiles, wood-paper products, flammable liquids, flammable gases, chemicals-plastics-metals, and forestry products. Moreover, the potential factors of combustible material categories are presented in Table 3-4. Flammable and combustible liquids are the top combustible materials in the Canadian SG-MURBs. Whereas building components are the top combustible materials in all types of residential buildings. On the other hand, flammable gases are the lowest combustible materials in all residential buildings and SG-MURBs. Note: All types of residential buildings include town housing, condominium, apartment, tenement, hotel, motel, single/semi-detached, n-plex, educational institution, and mobile home/trailer park. Figure 3-9: Percentages of CMs-FIs for all types of residential buildings and SG-MURBs in Canada between 2005 and 2015 17.1%10.2%6.7%12.7%15.4%1.4%4.4%6.2%26.0%8.2%14.7%7.5%11.4%16.7%0.8%2.8%4.6%33.2%Building componentsFurniture, furnishingsClothing, textilesWood, paper productsFlammable liquids, combustible liquidsFlammable gasesChemicals, plastics, metalsAgricultural, forestry productsMiscellaneousCategories of combustible materialsSG-MURBs All types of residential buildings 44 Table 3-8: Combustible materials in SG-MURBs Combustible materials References BUILDING COMPONENTS Roof covering [20][32][34][93][231][245] [248][249][251][285]–[294] Exterior wall covering, surface, finish - includes eave (prevents water from leaking into the home) Exterior trim and appurtenance (includes door, porch, platform, balcony) Floor covering - carpet, rug Building component - floor, ceiling, roof (excludes floor covering, ceiling covering) Building component – wall (excludes exterior wall, interior wall covering) Interior wall covering (excludes plastic) Insulation - wood, wood product - rigid (includes cellulose insulation; excludes wood shavings, sawdust) Insulation - mineral (includes binder, envelope) FURNITURE, FURNISHINGS Furniture - upholstered (includes chesterfield, sofa, davenport, couch) [32][170][230][243][248][249] [252][280][295]–[297] Furniture - wood - not upholstered (includes a table, bookcase, desk, chest of drawers, cabinet, dresser, filing cabinet, piano, organ, not upholstered chair) Mattress (includes hide-a-bed mattress, box spring) Bedding (includes a blanket, sheet, comforter, eiderdown, heating pad, pillow) Drapery (includes curtain, blind, tapestry) Broom, mop, brush CLOTHING, TEXTILES Clothing - cotton [32][280][298]–[304] Clothing - synthetic fibre - includes mixed fibres Fabric - cotton (includes finished goods; excludes fibre, yarn, clothing, bedding, drapery) Fabric – natural, unspecified Fabric - synthetic fibre (includes finished goods; excludes fibre, yarn, clothing, bedding, drapery) WOOD, PAPER PRODUCTS Wood (includes box, barrel, case, finished lumber, plywood; excludes structural component, furniture, furnishings and felled timber) [32][35][293][305]–[308] Paper, packing material (includes shredded paper) Wastepaper (excludes garbage, trash classified) Cardboard (includes carton, box) Paper stock (includes stationery supplies, files, book, magazine, newspaper, writing paper) FLAMMABLE LIQUIDS, COMBUSTIBLE LIQUIDS Gasoline [32][34][59][127] [243][257][269][280][292][295] [309]–[314] Diesel Paint, varnish Cooking oil, fat Tar, asphalt (includes pitch, bitumen) FLAMMABLE GASES Natural gas [310][315]–[319] Propane (includes butane, liquefied petroleum gases) CHEMICALS, PLASTICS, METALS Plastics (excludes cellulose nitrate) [226][281][320][321] AGRICULTURAL, FORESTRY PRODUCTS Grass, brush, leaves [31][294][322] MISCELLANEOUS Coke, coal [20][32] [74][87] [222][242][243][248][249][280] [321][323]–[326] Creosote (including in chimney) Insulation, electric - confined to electrical equipment (includes only when confined to electrical equipment) Garbage, trash, rubbish 45 3.2.1.4. Factors of the discovery of fire and actions taken Fire safety techniques identification focuses on identifying, selecting, and enhancing the best practices for efficient fire control. The initial fire detection was found to be by, but not limited to, a smoke alarm device, smoke detector device, and heat alarm device [34][269][327]. Identification of the device performance can be taken from different angles, including if the equipment operated, equipment should have operated but did not, and equipment present but fire too small to require operation [32][223][328]. Figure 3-10 presents and compares six main techniques used to be taken into account to control fire impacts in the Canadian SG-MURBs, including handheld extinguisher, standpipe and hose systems (building systems), makeshift fire fighting aids, fire department – water including, and sprinkler system using several suppression materials [119][223][329]. Figure 3-10 depicts potential factors of the discovery of fire and actions taken under each category of methods of fire control in SG-MURBs. Note: All types of residential buildings include town housing, condominium, apartment, tenement, hotel, motel, single/semi-detached, n-plex, educational institution, and mobile home/trailer park. 11.1%1.5%14.8%50.4%3.6%1.3%0.2%10.9%6.2%14.6%2.4%18.5%32.5%3.3%4.3%0.2%15.6%8.6%Handheld extinguisherStandpipe & hose systems (building)Makeshift firefighting aidsFire department - waterFire department – other than waterSprinkler systemFixed system other than sprinklersBurned outMiscellaneousMethods of fire controlSG-MURBs All types of residential buildingsFigure 3-10: Percentages of MFC for all types of residential buildings and SG-MURBs in Canada between 2005 and 2015 46 Figure 3-11: Investigating factors of the discovery of fire and actions taken 47 Several studies have investigated fire safety strategies, as summarized in Table 3-9. The studies' investigation covers different aspects, including the nature of the study, building type, the study scope, and investigated fire strategies. Table 3.5 shows that limited strategies have been considered in the existing studies. Fire investigation Nature of study Journal article Research chapter Research chapter Journal article A case study Conference article Journal article Building type Residential buildings Buildings Informal settlements Urban Six-storey commercial building Industrial Building Different building layouts Study scope Fire deaths Fire protection measures Intervention Fire safety management Probabilistic Risk Assessment Probabilistic Risk Assessment Human evacuation performance Fire safety strategies factors INITIAL DETECTION Smoke alarm device - √ √ - √ √ √ Heat alarm device √ - √ - - - - Automatic sprinkler system √ √ √ - √ √ - Visual sighting or other means of personal detection - - - - √ - - ACTION TAKEN Extinguish by occupant - √ - - √ √ - Extinguish by fire department - - √ - - - - Extinguish by automatic system √ - √ - √ √ - METHOD OF FIRE CONTROL AND EXTINGUISHMENT Handheld extinguisher - √ - - √ √ - Standpipe & hose systems - √ - - √ - - Makeshift firefighting aids √ - - - - - - Fire department - water - √ √ - - √ - Fire department – other than water - - - - - - - Sprinkler system √ √ √ - √ √ - PERFORMANCE OF SMOKE ALARM DEVICE Alarm in room of origin - activated √ √ √ - √ - √ Alarm not in room of origin - activated √ - - - √ - √ Alarm in room of origin – not activated – non-suitable location √ - - - - - - Alarm in room of origin – not activated – battery dead √ - - - - - - Alarm in room of origin – not activated – no battery √ - - - - - - References [80] [79] [330] [331] [332] [333] [334] Table 3-9: A summary of studies investigated fire safety strategies 48 The most common suppression materials are (i) Water, (ii) Foam, which mainly contains fluorochemical surfactants that help control the physical properties of water, allowing it to float and spread across the hydrocarbon liquid surface. (iii) Dry powder using mono-ammonium phosphate, which is a dry chemical. (iv) CO2 as pure carbon dioxide, and (v) Wet Chemical uses a pressurized solution of alkali salts, the produced salts from the product of the neutralization of a strong base and a weak acid, in water. Extinguishing systems use two different techniques to release the stored suppression material inside the body of the extinguisher. The first technique uses a pressurized CO2 gas cartridge (60 g) for a 9-litre extinguisher; the second technique uses pre-pressurized with up to 10 bars using dry compressed air, such as nitrogen. 3.2.2. Fire risk analysis Fire risk analysis is the process of estimating the relative frequency of fire and probabilities of the adverse effects resulting from fire incidents [335]. Besides, it predicts possible fire impacts that help regulatory decision-making to reduce the risk [78]. Risk analysis covers a series of activities, including analyzing the relative frequency of contributing factors, their impacts magnitude, and uncertainties. Fire risk analysis can be expressed either in a qualitative, semi-quantitative, or quantitative approach, depending on the type of risk, the purpose of risk analysis, and the availability of information resources [36]. A summary of the well-known methods of fire risk analysis approaches is presented. Qualitative methods include hazard identification (HAZID), analysis of failure modes, effects and criticality (AMDEC), preliminary hazard analysis (PHA), hazard analysis (HAZAN), workplace risk assessment and control (WRAC), job safety analysis (JSA), checklist, and what-if analysis [131][200][201] [336][337]. Semi-quantitative methods combine the advantages of qualitative and quantitative 49 methods [338]. Semi-quantitative methods give an intermediate level between qualitative, which is textual evaluation and quantitative, which is a numerical evaluation. Different methods were used for a semi-quantitative approach, such as a layer of protection analysis (LOPA), which is used to evaluate the existing or propose layers of protection against known hazards [69][339]–[351]; frequency number (FN) curves; risk-index, and risk-matrix [352][353][353]. Quantitative methods are developed for the precision of accident-likelihood and consequence-severity [131]. Some of the considered quantitative methods are presented in this section as follows: fault tree analysis (FTA) as a deductive method to analyze the causes of a fire risk [354]–[357]. Event tree analysis (ETA) is an inductive method to analyze the possible consequences of fire risk [350][358][359]. Bowtie method is developed to analyze risk scenarios quantitatively, from causes to consequences [71][359][360]. 3.2.3. Fire risk evaluation Fire risk evaluation decides the fire risk level using the developed fire risk criteria. Fire risk evaluation as the third step of FRA helps estimate the risk level based mainly on its likelihood and gravity. Several studies have been conducted in FRA to enhance fire protection, considering deterministic and stochastic approaches. Hansen et al. evaluated fire risk for residential high-rises and suggested having detailed statistical data for a better estimation [90]. Villani and Miyagi proposed a modelling procedure of fire protection systems using the Petri net method, suggesting integrating other variables, such as CMs-FIs, to enhance fire protection [361]. Fang et al. assessed the fire protection system's performance using the entropy weight method; it was suggested to use appropriate fire control methods to enhance fire protection of people and properties effectively [59]. Nilsson developed a method for fire safety evaluation focusing on the protection of multifunctional buildings. It was suggested to investigate the 50 effect of combustible materials [74]. James et al. investigated buildings and occupants' performance during a fire, signifying the necessity to explore fire protection features [38]. The World Health Organization provided guidance to meet and enhance fire protection, suggesting to select suitable fire protection equipment in all areas [362]. Pei et al. used event tree analysis and Bayesian network for FRA; it was recommended to investigate property loss to determine fire risk value [100]. Judith et al. provided an assessment method for fire protection focusing on combustible facades to assess fire impacts; it was recommended to establish an appropriate mechanism to investigate combustible materials to enhance fire protection [281]. Andika and Ratnayake conducted a risk-based inspection screening assessment; it was suggested to incorporate artificial neural networks for risk assessment to improve prediction strength [102]. Fowell et al. investigated the severity and impact of building fires focusing on the flammability of furnishings and contents of buildings. It was suggested to incorporate performance assessment of fire protection systems to control the severity and impact of fires [248]. Zheng et al. established a comprehensive fire risk evaluation index using the analytic hierarchy process; it was suggested that a neural network is feasible for fire risk assessment [103]. Furthermore, fire risk assessment and prediction tools have been developed in order to enhance fire protection. For example, FiRECAM [51] was developed for fire risk evaluation and cost assessment model to help evaluate two main aspects, a risk to occupants' lives and building fire costs. CESARE-Risk [52] was established for building fire safety systems to identify possible cost-effective design solutions. FIERA system [53][54] was developed to help evaluate fire protection systems in industrial buildings. CUrisk was developed at Carleton University, Canada, to evaluate the overall fire risk of a building considering the available active and passive fire protection systems [55]. 51 3.2.4. Fire safety strategies Fire safety strategies enhance risk treatment to improve existing fire safety measures and propose new fire safety strategies to reduce fire risk. Several fire safety technologies were developed as barriers to control spreading fires in a significant way using detection and video technologies, water mist system, integrated voice evacuation and messaging system [236] [278][279][363]–[375]. Five main response strategies are taken into account to treat fire risk factors: accepted, controlled, mitigated, shared, or transferred (Figure 3-12). The response strategies are based on several parameters, including relative frequency, related impacts, vulnerability, and criticality of each factor. Accept strategy is to admit the fire risk factors under very low (VL) grade or based on their relative frequencies and control their possible negative impact. A control strategy is a plan to control threat impact and relative frequency to keep the fire risk factors under low grade (L). Mitigate strategy refers to mitigating the relative frequency of fire factors under medium (M) grades and their severity through different actions, such as fire plans and regulations, fire protection systems, education programs, and preparedness and response actions. Share strategy is a share of the critical fire factors with a third party to share fire risk impacts to enhance fire safety for the shared fire factors, such as extinguishers, building design, appropriate training, and improve fire safety policies. Finally, the transfer strategy refers to transferring the critical fire factors under very high (VH) grade entirely to a third party or parties that can manage fire risk outcomes through insurance contracts. As shown in the previous phases for prevention and protection purposes, the analyzed fire risk factors will be evaluated in this phase to identify critical factors and select the appropriate safety strategies (Figure 3-12). This phase is also assessing the performance of different fire intervention 52 techniques, including standpipe and hose systems, makeshift firefighting aids, and fire department. Investigating the intervention systems' performance and their capacity to control fires and reduce their impacts is essential for fire risk management. Risk response strategiesAcceptControlMitigateShareTransferIncendiary firesRelative frequencyRelated impactsVulnerabilityCriticalityHuman failingMisuse of equipmentMisuse of source of ignitionMisuse of material ignitedFlammable liquidsFlammable gasesWood and paper productsnFire risk ignition sources and combustible materialsFire safety regulationsUpdatesEnhance fire prevention, protection, and intervention strategiesImprove the current practice of decision making Improve and overcome several shortcomings Help planners and developers to select, install, and invest in the most safety systemsFire risk management plan 3.3. Challenges and Sources of Uncertainties The sources of FRA uncertainties are classified as completeness, parameter, and modelling [66][67][70]–[72]. The uncertainties are found to be related to all the FRA phases, including fire risk identification (hazard identification and likelihood identification), fire risk analysis (causes and consequences analysis), and fire risk evaluation [376]–[378]. Hazard identification defines potential causes and unwanted consequences. Many techniques are used for hazard Figure 3-12: Response strategies for fire risk treatment 53 identification, such as Failure Mode and Effects Analysis (FMEA). The uncertainty in the hazard identification process is due to the completeness, parameters, and modelling. The details of the uncertainties for hazard identification are as follows: ▪ Completeness: inability to identify all the fire risk contributors as well as errors in the screening of fire hazards ▪ Parameter: imprecision or vagueness in characteristic properties of fire contributors ▪ Modelling: lack of interaction between different fire contributors in scenario models Likelihood analysis determines the relative frequency of fire risk factors and their specific consequences [379]–[381]. The details of the uncertainties for likelihood analysis are as follows: ▪ Completeness: incorrectness in selecting fire risk factors, fire safety barriers, and number of fire risk outcome ▪ Parameter: lack of real-time data for fire risk factors ▪ Modelling: complexity phenomena, inadequacy, and imprecision of the relative frequency modelling for the fire risk source factors to the possible consequences Risk analysis refers to analyzing possible causes of fire occurrence and its consequences on people, properties, and the environment. Many methods could be used for the analysis, such as the Bowtie method. The details of the uncertainties for risk analysis are as follows [75][211][214] [382][383]: ▪ Completeness: incorrectness in the identification of all types of causes and consequences as well as their interactions ▪ Parameter: lack, insufficiency or vagueness in values of fire risk model factors 54 ▪ Modelling: incorrectness analysis of FTA and ETA leading to inadequate Minimal Cut Set as well as imprecision modelling interactions among outcomes Risk evaluation refers to the risk acceptance process using different methods, such as risk-index and risk-matrix. The uncertainty could be due to completeness, modelling, and parameters [212][384]–[387]. The details of the uncertainties for risk evaluation are as follows: ▪ Completeness: incorrectness in the interpretation of results ▪ Parameter: fire risk and safety results not based on real-time data ▪ Modelling: inadequacy in the selection of fire risk attribute and its safety measures as well as its acceptance criteria 3.4. Quantification of Uncertainties The above-mentioned uncertainties in FRA, as seen in part 3.3, are referring to uncertainty in an experiment or obtained data on which are used in a model or modelling itself. Whereas the uncertainty includes inaccuracy, lack of precision, sampling and analysis errors. In general, these uncertainties are classified into two categories; Aleatoric and Epistemic uncertainty. Aleatoric uncertainty is also named statistical uncertainty and refers to the difference in the output for each time the experiment is launched due to inherent uncertainty that could come from different data resources. Whereas epistemic uncertainty, which is also named systematic uncertainty, is referring to the limited data and knowledge [377][378]. The uncertainty could be presented in a range as follows: measurement = best estimate ± uncertainty. Quantifying uncertainties is beneficial to fire risk assessment in preventing and protecting people, the environment and properties effectively. The benefits of the quantified uncertainties in fire risk assessments could be summarized as follows [376][388][389]: 55 (i) Identify and control fire hazards in a building. (ii) Identify specific prevention measures according to the identified hazards. (iii) Save costs by taking into account proactive instead of reactive safety measures. (iv) Define effective plans for training, inspections, and emergency. (v) Manage a building effectively according to the fire risk assessment. (vi) Analyze all possible risks that could affect the occupants and the surrounding neighbourhoods. (vii) Analyze all possible fire risk outcomes Quantification of these uncertainties could be done through different techniques. Quantifying aleatoric uncertainty is based on modelling the probability of different outcomes in a scenario that could reach thousands of simulations, using methods, such as Monte Carlo simulation (MCS). MCS (or probability simulation) builds models of possible results to perform the analysis using all the inherent uncertainty factors [63]. MCS processes the approximating outcomes through the repetitive random application of a model’s algorithm. To quantify the Epistemic uncertainty, different methods might be used, such as (i) fuzzy logic to analyze a set of possible values with confidence estimation. (ii) Bayesian network (BN) is a probabilistic method used to deal with different types of uncertainties using evidential reasoning instead of using random processes [390]–[394] Dempster–Shafer theory (DST) is a theory of belief function that is referred to as evidence theory [395]–[397]. DST is considered to be a general framework for reasoning with uncertainty [396]. DST takes all the available evidence by combining evidence from different foundations to define a degree of belief representing a belief function. Grey relational analysis (GRA), also called Deng's Grey Incidence Analysis model [249][398]–[400], is developed 56 using the grey number (GN) as a technique to handle data uncertainties presenting the possible data values in a range rather than exact values [398][401]–[403]. GRA defines situations with no information and perfect information as black and white, respectively. Whereas situations between the two extremes with partial information are considered to be grey. The Grey System Theory enables to (i) model, (ii) analyze, (iii) monitor, and (iv) control such partially defined systems by generating useful information from the available data [404][405]. Moreover, the presented literature shows that investigating fire risk and safety factors using machine learning algorithms with real-world data would help handle the challenges mentioned above. Furthermore, this would be essential in controlling fire impacts and enhancing fire protection of people, properties, and buildings' environment. Artificial neural network (ANN) is found by several researchers to be a powerful machine learning technique that uses nonlinear functions with good potential in modelling complex and nonlinear relationships among the model’s predictor and response variables [406][407]. Izquierdo et al. used ANN to reduce the inherent epistemic uncertainty to reliability estimations [408]. ANN is also found to be helpful in reducing the associated uncertainties with results [409]. The ANN was found by Pedroni et al. to be the most effective in estimating the failure probability compared with quadratic response surfaces. ANN helped Santhosh et al. enhance prediction for safety assessment purposes [68]; besides, it helped Ding et al. develop a smart fire risk estimation model focusing on factors index for fire risk assessment in high-rising buildings [410]. The literature shows that investigating fire risk and safety factors using ANN with real-world data would help handle the related challenges to the traditional techniques, such as fault tree analysis, Bayesian approach, and Bowtie method [411]–[413]. To prove the robustness of ANN, Table 3-10 is added, comparing ANN with the most known quantitative risk methods. Moreover, ANN was 57 found to outperform several techniques, including linear time-series model, regressions, and radial basis function [414]. Table 3-10: Comparison between ANN and most known quantitative risk methods Methods Criteria References Scenario analysis Predictors and responses Modelling complex Nonlinear relationships Epistemic uncertainty Aleatory uncertainty Prediction Precision Fault tree analysis √ Limited - - √ - √ - [99], [415] Reliability block diagram √ Limited Limited - - √ √ - [416], [417] Artificial neural network √ √ √ √ - - √ √ [381], [418] Event tree analysis √ Limited - - √ - √ - [419] Bayesian network √ Limited Limited √ - √ √ √ [420] Bowtie analysis √ Limited - - √ - √ - [412] F-N Curves √ Limited Limited - - √ √ - [352] Regression analysis √ √ √ √ - - √ - [421] 58 CHAPTER 4: AN INTEGRATED RISK ASSESSMENT APPROACH FOR FIRE IGNITION SOURCES 4.1. Background Fire ignition sources cover events with the potential of causing fires in SG-MURBs, including heating equipment, cooking equipment, electrical distribution equipment, smokers materials, and open flame [82][241]–[243][254]–[256]. In Canada, 42% of fire incidents in SG-MURBs are due to cooking equipment, and smokers' materials and open flame caused 35% of fires. Investigation of ignition sources supports safety strategies and helps control a fire in less time [34]. Moreover, identifying the potential ignition sources improves systems' reliability and minimizes their relative frequencies [38]; the national fire database is the main derivative source of ignition frequency [240]. Defining the relative frequency of ignition sources is essential for quantitative estimation and prediction of fire risk, which helps select effective prevention techniques accordingly [238]–[240]. A comprehensive fire risk assessment of fire ignition sources would help control fire causes; furthermore, fire codes and regulations require enhancing fire prevention strategies. A fire risk assessment (FRA) is a systematic process of three main steps, including identification, analysis, and evaluation of ignition source factors that induce fire incidents (ISFs-FIs) [58][422]. FRA considers relative frequencies and fire impacts to determine the criticality of factors that help support any fire prevention decisions accordingly [423]. A comprehensive literature review (chapter 3) showed limited models in the published literature to predict fire-related responses, including fire impacts, in SG-MURBs. Therefore, a comprehensive quantitative FRA approach is required for SG-MURBs incorporating uncertainties in the analysis, which can assist in preparing and improving fire prevention strategies accordingly. Several techniques can be used for prediction, including regression 59 models and artificial neural networks (ANN) [413][424][425]. ANN can be the best suitable technique in this case, as many responses and predictors are involved in the model. This study intends to answer four critical questions to enhance fire prevention in SG-MURBs: (i) What are the key potential ISFs-FIs in SG-MURBs? (ii) How can ISFs-FIs be evaluated quantitatively to determine the most critical factors? (iii) What are the most significant predictors for the possible consequence, cause failure escape, and nature of casualties? (iv) How to predict the responses effectively and efficiently? 4.2. Methodology This study developed a fire risk assessment approach (Figure 4-1) to answer the three questions mentioned above. The approach consists of three main steps: (i) identify the potential ISFs-FIs in SG-MURBs and analyze their relative frequencies with the related dollar loss; (ii) establish benchmarks for the relative frequencies and the related dollar loss to evaluate ISFs-FIs; (iii) develop models using several classifiers and artificial neural network for fire impacts prediction. In the context of this study, ISFs-FIs refer to the actual equipment, device or item which can initiate ignition. Details of the developed approach are presented in the following sub-sections. 60 IdentificationCategorizationAnalysis EvaluationF1F2FnGroupsCOOKING EQUIPMENT APPLIANCES AND EQUIPMENT ELECTR DISTRIBUTION EQUIPMENT ELECTRICAL EQUIPMENT SMOKER'S MATERIAL AND OPEN FLAME EXPOSUREHEATING EQUIPMENT MISCELLANEOUSGISs - FIsISFs - FIsSISs - FIsInvestigate all potential ignition sources factors in SG-MURBsRelative frequency and impactsFitting distributionPercentiles of the probability distributions Levels identificationCriticality evaluationComparison assessmentAttributesANN modelPerformanceTraining stateError histogramRegressionSimulation ANN-based modellingACSECCICOFTFMICFEPCNCClassificationConfusion matrixROC curveModels accuracyCorrelationSpearman correlationP-value Note: AC: age group categories, SE: sex, CC: contributing condition, ICOF: ignition of clothing or other fabrics, and TFMI: type of fabric or material that might be ignited, PC: possible consequence, CFE: cause failure escape, and NC: nature of casualties. 4.2.1. Categorization and identification of potential ISFs-FIs The fire risk factors are categorized under eight attributes taking into consideration green-ignition sources (GISs-FIs) and smart-ignition sources (SISs-FIs) (Figure 4-1). The relative frequencies of the potential ISFs-FIs are determined for a specific city using the following equations: 𝐹𝑟𝑖 =𝑁𝑖∑ 𝑁𝑖 𝑛𝑖=1⁄ ∀𝑖 = {1,2, … , 𝑛} 𝐸𝑞(4 − 1) 𝑇𝐷𝐿𝑖 =∑ 𝑇𝐷𝐿𝑖 𝑛𝑖=1𝑁𝑖⁄ ∀𝑖 = {1,2, … , 𝑛} 𝐸𝑞(4 − 2) where 𝐹𝑟𝑖 presents the relative frequency of the ith ISF-FIs; Ni indicates the number of fire incidents caused by a specific (ith) ISFi-FIs per year in one city; and ∑ 𝑁𝑖 𝑛𝑖=1 presents the total number of fire incidents in the same city; 𝑇𝐷𝐿𝑖 presents total dollar loss related to the ith ISF-Figure 4-1: Integrated risk assessment approach for fire ignition sources in Smart-Green Multi-Unit Residential Buildings 61 FIs. The relative frequencies can be defined at the city scale as in this study; however, any other geographical scale, such as a province or country, can be used based on the available data. 4.2.2. Benchmarking: analysis and evaluation Benchmarks are developed for relative frequencies and related total dollar loss (TDL) per year of ISFi-FIs. Fire incidents data, including relative frequency and dollar loss of different cities, are used for benchmarking. At first, probability density functions (PDF) are defined by fitting the relevant PDFs for each specific ISF-FIs. Akaike Information Criterion (AIC) is used as an estimator to determine the best-fit of a distribution, where a PDF with the lowest value of AIC is selected [416][417]. The suitable PDF is identified for the relative frequency of each ISF-FIs using @RiskTM 7.6 (Palisade Corp., USA) [428]. The relative frequencies are classified into five levels very low (VL = 1), low (L = 2), medium (M = 3), high (H = 4), and very high (VH = 5) for the percentiles of the relative frequencies <10%, 10-30%, 30-50%, 50-70%, and >=70%, respectively. The relative frequencies of ISFi and the related TDL were assigned with the relevant frequency class. The risk level of the potential CMs-FIs is determined for a specific city using the following equation: 𝑅𝑖 = (𝐿𝐹𝑟)𝑖 ∙ (𝐿𝑇𝐷𝐿)𝑖 ∀𝑖 = {1,2, … , 𝑛} 𝐸𝑞(4 − 3) where 𝑅𝑖 is the risk level of the ith ISF-FIs; (𝐿𝐹𝑟)𝑖 presents a level of relative frequency for factor (i); and (𝐿𝑇𝐷𝐿)𝑖 is the level of TDL for the related potential ISF-FIs (i). The risk level for the potential factors varies in ranges of [1 5), [5 10), [10 15), [15 20), [20 25] for VL, L, M, H, and VH, correspondingly. 62 The established benchmarks assist decision-makers to know the degree of relative fire frequencies and dollar loss specific to an ignition factor against the typical levels of common cities. It helps cities to improve fire safety measures. In addition, decision-makers can use benchmarks to identify the level of the ISFs-FIs, compare fire ignition levels between cities, determine critical factors, install appropriate fire suppression materials, and identify innovations that would help improve fire safety strategies. 4.2.3. Prediction analysis: classification and ANN-based regression modelling 4.2.3.1. Correlation and association analysis Correlation and association analysis was performed among several predictors and responses. Then, variables with a small effect on the predictors are avoided, considering p-value and multicollinearity. In this study, variables with p-value =< 0.05 and a spearman correlation coefficient > 0.5 were selected. Consequently, different responses were predicted, including possible consequence (PC), cause failure escape (CFE), and nature of casualties (NC) using multiple predictors, such as ignition of clothing or other fabrics (ICOF), type of fabric or material that might be ignited (TFMI), age group categories (AC), sex (SE), and contributing condition (CC) in SG-MURBs. 4.2.3.2. Classification-based risk exploration Supervised machine learning-based classification is implemented in this study to help explore fire incident data, select potential factors, train models, and assess fire impacts using MATLAB® R2018b. Moreover, classification is used to predict responses using various classifiers (Figure 4-2), including decision trees, discriminant analysis, support vector machines, nearest neighbors, and ensemble classification. First, a decision tree is one of the common classification algorithms that build a tree form for structure-based classification or 63 regression models. Decision trees help structure data set to different subsets as a tree with decision and leaf nodes; whereas a decision node could have two or more branches, and a leaf node presents a classification. Decision trees are found to have abilities in handling both categorical and numerical data. Different types of trees might be integrated, including a fine tree, medium tree, and coarse tree. Second, the discriminant analysis uses Gaussian distributions to generate different classes using different algorithms, including linear discriminant and quadratic discriminant. Third, a support vector machine (SVM) is a robust supervised learning method in machine learning that helps analyze, classify, and predict data. SVM has an efficient ability to perform linear and non-linear classification mapping inputs into high-dimensional feature spaces using different algorithms, such as Linear SVM, Quadratic SVM, Cubic SVM, Fine Gaussian SVM, Medium Gaussian SVM, and Coarse Gaussian SVM. Fourth, k-nearest-neighbors (KNN) algorithm is a known supervised machine learning technique for classification using the proximity of ‘sameness’. The KNN defines the closest labelled points to a new point as its nearest neighbor using a dissimilarity function that is based on the geometric distance. Different KNN algorithms might be used, including Fine KNN, Medium KNN, Coarse KNN, Cosine KNN, Cubic KNN, and Weighted KNN. Fifth, ensemble classifiers as meta-algorithms are considered the strategic combination process of several machine learning algorithms from different classifiers to solve a specific problem and improve the classification and prediction. The most known algorithms of ensemble classifiers are Boosted trees, Bagged trees, Subspace discriminant, Subspace KNN, and RUSBoosted Trees. Selecting the best classifiers is based on results' performance, including the confusion matrix, ROC curve, and evaluation of models’ accuracy. 64 StartHistorical dataDefine variablesDetermine predictors and responsesClassification AnalysisCorrelation and association analysisP-valueStatistical analysisLinear discriminantDiscriminant analysisQuadratic discriminantSelect the best modelEndFine treeDecision treesMedium treeCoarse treeRUSBooster treesSubspaceSubspace discriminantBagged treesBoosted treesEnsemble classifiersWeighted KNNCubic KNNCosine KNNCoarse KNNMedium KNNFine KNNNearest neighbor classifiersCoarse Gaussian SVMMedium Gaussian SVMFine Gaussian SVMCubic SVMQuadratic SVMLinear SVMSupport vector machinesROC CurveConfusion matrixPlotsAccuracy of the modelsResultsn modelsm AlgorithmsSVM: support vector machine.KNN: K-nearest neighbors; K is the number of clusters.ROC curve: receiver operating characteristic curve. Figure 4-2: Classification-based machine learning for fire ignition sources analysis 65 A confusion matrix shows the number or percentage of observations in each cell describing a classifier's performance in each class to identify the areas where the classifier has performed poorly. The rows of the matrix show the true classes, whereas the columns present the predicted classes. The matrix's diagonal cells present the match of true and predicted classes; the cells are shown with green colour for a well-performed classifier. The True Positive Rates and False Negative Rates options show the classifier's performance; the last two columns of the matrix on the right shows summaries per true class. The receiver operating characteristic (ROC) curve is used to show the True Positive Rate (TPR) and False Positive Rate (FPR) for the trained classifier and select different classes to plot. The ROC curve presents the perfect results, with no misclassified factors, as a right angle to the plot's top left. Whereas the poor result is a line at 45 degrees [429]. The ROC curve presents the trade-off between sensitivity that is presented by TPR (or recall (REC)) and specificity (1 – FPR). The sensitivity is considered as the number of correct positive predictions over the total number of positives; 1.0 and 0.0 refer to the best and the worst sensitivity, respectively. Classifiers that are closer to the top-left corner of the ROC curve indicate better performance. The evaluation of a model's accuracy is determined based on the number of correct predictions over the total number of predictions (Eq (4-4)). The performance of a model could be determined using an error rate (Eq (4-5)). The best classification algorithms have the highest accuracy, or equivalently with the lowest error rate. 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑟𝑟𝑒𝑐𝑡 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠 Eq (4 − 4) 𝐸𝑟𝑟𝑜𝑟 𝑟𝑎𝑡𝑒 =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑤𝑟𝑜𝑛𝑔 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠 Eq (4 − 5) 66 The code to predict possible consequence (PC), cause failure escape (CFE), and nature of casualties (NC) using the predictors type of ignition of clothing or other fabrics (ICOF), type of fabric or material that might be ignited (TFMI), age group categories (AC), sex (SE), and contributing condition (CC) is presented as follows: function [trainedClassifier, validationAccuracy] = trainClassifier(trainingData) inputTable = trainingData; predictorNames = {'ICOF', 'TFMI', 'AC', 'SE', 'CC'}; predictors = inputTable(:, predictorNames); response = inputTable.AFO; isCategoricalPredictor = [false, false, false, false, false]; classificationTree = fitctree(... predictors, ... response, ... 'SplitCriterion', 'gdi', ... 'MaxNumSplits', 20, ... 'Surrogate', 'off', ... 'ClassNames', [1; 2; 4; 5; 6; 7; 8; 9]); % Create the result struct with predict function predictorExtractionFcn = @(t) t(:, predictorNames); treePredictFcn = @(x) predict(classificationTree, x); trainedClassifier.predictFcn = @(x) treePredictFcn(predictorExtractionFcn(x)); % Add additional fields to the result struct trainedClassifier.RequiredVariables = {'ICOF', 'TFMI', 'AC', 'SE', 'CC'}; trainedClassifier.ClassificationTree = classificationTree; trainedClassifier.About = 'This struct is a trained model exported from Classification Learner R2018b.'; trainedClassifier.HowToPredict = sprintf('To make predictions on a new table, T, use: \n yfit = c.predictFcn(T) \nreplacing ''c'' with the name of the variable that is this struct, e.g. ''trainedModel''. \n \nThe table, T, must contain the variables returned by: \n c.RequiredVariables \nVariable formats (e.g. matrix/vector, datatype) must match the original training data. \nAdditional variables are ignored. \n \nFor more information, see How to predict using an exported model.'); % Extract predictors and response % This code processes the data into the right shape for training the % model. inputTable = trainingData; predictorNames = {'ICOF', 'TFMI', 'AC', 'SE', 'CC'}; predictors = inputTable(:, predictorNames); 67 response = inputTable.AFO; isCategoricalPredictor = [false, false, false, false, false]; % Perform cross-validation partitionedModel = crossval(trainedClassifier.ClassificationTree, 'KFold', 5); % Compute validation predictions [validationPredictions, validationScores] = kfoldPredict(partitionedModel); % Compute validation accuracy validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError'); 4.2.3.3. Artificial neural network models Artificial neural network (ANN) is a computing system technique in machine learning. ANN is considered to be the component of artificial intelligence to simulate the functioning of a human brain. ANN is found to be a robust method that could handle a large amount of data set. Furthermore, ANN can implicitly detect complex nonlinear relationships among dependent and independent variables detecting all possible interactions. Different equations are used to help ANN in increasing prediction accuracy performance, as follows: 𝑦 = 𝑓(𝜑) = 𝑓 (∑(𝑤𝑖𝑚𝑖=1∗ 𝑎𝑖) + 𝑏) 𝐸𝑞(4 − 6) Where y is the neuron output; 𝑓(𝜑) is the function of summation output. 𝑎𝑖 and 𝑤𝑖 refer to the inputs and weights, respectively. Weights as initial values are assumed then determined using error backpropagation at minimum error between outputs and targets. b refers to bias, a constant that is added to the weighted sum of the inputs to adjust the output accordingly to help the model fit best for the data. 𝜑 = 𝑊𝑇 ∗ 𝐴 𝐸𝑞(4 − 7) 68 𝑊𝑇represents the transpose of weights vectors; where W = [𝑤1, 𝑤2, 𝑤3, … , 𝑤𝑚], and A represents inputs vectors; A = [𝑎1, 𝑎2, 𝑎3, … , 𝑎𝑚]. Weights are the parameters within an artificial neural network that converts predictors data within the network's hidden layers. Different activation functions might be used to define the output given set of inputs, including Sigmoid, Tanh (Tangent Hyperbolic), and RELU (Rectified Linear Unit). 𝑆𝑖𝑔𝑚𝑜𝑖𝑑: 𝑓(𝑥) =11 + 𝑒−𝑥 𝐸𝑞(4 − 8) where x ranges from -1 to 1, and 𝑓(𝑥) ranges from 0 to 1. 𝑇𝑎𝑛ℎ: 𝑓(𝑥) =21 + 𝑒−2𝑥− 1 𝐸𝑞(4 − 9) where x ranges from -1 to 1, and 𝑓(𝑥) also ranges from -1 to 1. 𝑅𝐸𝐿𝑈: 𝑓(𝑥) = {0 𝑓𝑜𝑟 𝑥 < 0𝑥 𝑓𝑜𝑟 𝑥 ≥ 0 𝐸𝑞(4 − 10) where x and 𝑓(𝑥) are both ranging from 0 to 1. Artificial Neural Network model is developed (Figure 4-3) for predicting fire impacts, including cause failure escape, and nature of casualties using MATLAB (R2018b) nprtool [430]. Five predictors are considered, including ignition of clothing or other fabrics (ICOF), type of fabric or material that might be ignited (TFMI), age group categories (AC), sex (SE), and contributing condition (CC). Potential variables are selected for better accuracy of the predictive model considering preliminary identification, knowledge-based, and analysis selection [102]. The preliminary identification avoids duplicate variables and variables with missing values. A variable is a duplicate when the same information is presented with another 69 variable. Whereas missing values can be due to several reasons, including unrecorded data, causing errors in the learning algorithms [102]. Regarding the knowledge-based selection, variables with meaningful information, based on experts and literature, and related to the main objective assessment are considered. The ANN model is developed using three algorithms, Lavenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) backpropagation algorithms to determine the best algorithm with high prediction accuracy [431]. The percentages of the data used for training, validation, and testing are 70%, 15%, and 15%, respectively; this split was the most preferred in the literature considering the total number of data samples and the actual training algorithms. Furthermore, the nprtool and nntool in MATLAB (R2018b) are randomly and by default dive up the data to 70%, 15%, and 15% for training, validation, and testing, respectively [430]. The performance of the model is determined using mean squared error (MSE), correlation coefficient (R), and coefficient of determination (R2). The rule of thumb to determine the strength of the model as very weak (VW), weak (W), moderately strong (MS), strong (S), very strong (VS), or perfect (P) corresponding to R values of < 0.2, < 0.4, < 0.6, <0.8, < 1, 𝑜𝑟 1, respectively [432]. The advanced script was generated to produce results and solve similar problems reusing the sequences of commands. Furthermore, MATLAB functions and Matrix-Only Function were produced as a code generation-based deployable solution of the trained ANN. Moreover, simulink models are generated to simulate ANN efficiently. In addition to the function fitting neural network, different components were integrated for efficient, robust system development and visual assessment of the predictors with the responses. 70 X1F(x)F(x)F(x)F(x)X2X3X4X5X6X7X8X9Xn Inputs (n)b1,1b1,2b1,3b1,m1st hidden layer F(x)F(x)F(x)F(x)b2,1b2,2b2,3b2,m F(x) Output2nd hidden layerb3,1W(2,1)/(1,1)W(2,1)/(m ,m)W(1,1)/(m,n)Output layerInput layerTraining state Error histogramPerformanceHistorical dataCorrelation and association analysisP-valueStatistical analysisDetermine predictors ANN ResultsRegression Simulation Possible consequencesCause failure escapeNature of casualitiesPredict responsesAvoid duplicate variablesVariables with valuesPreliminary identificationRelated assessment variables Include meaningful variablesKnowledge-based selectionAnalysis and selection of variablesVariables identification processAnalysis of variables: predictors and responsesAvoid multicollinear variablesInclude variables with a varianceFilter method selectionDefine variablesModelling analysis Algorithm selectionBest performance modelW1WmW2 bSumWeightsQ(.)Activation functionOutputYDetermine predictors Predict responses Figure 4-3: Artificial neural network approach for fire ignition sources analysis 71 A sine wave component was used to generate and describe a smooth periodic oscillation of 100 possible scenarios on a dashboard scope concerning the predictors. Besides, the codes of the predictors are presented using half gauge components. Regarding the responses, the results of the scenarios are presented using dashboard scopes, whereas gauge components are used to visualize precisely the code of the attribute for the last scenarios. An advanced script is generated to train ANN using BR algorithm for prediction of fire origin and ignition sources-based human factors as follows: % Define variables: Inputs5-input data; Outputs4-target data. x = Inputs5; t = Outputs4; trainFcn = 'trainbr'; % Bayesian Regularization backpropagation. % Create a Fitting Network hiddenLayerSize = 40; net = fitnet(hiddenLayerSize,trainFcn); net.input.processFcns = {'removeconstantrows','mapminmax'}; net.output.processFcns = {'removeconstantrows','mapminmax'}; net.divideFcn = 'dividerand'; % Divide data randomly net.divideMode = 'sample'; % Divide up every sample net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; net.performFcn = 'mse'; % Mean Squared Error net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ... 'plotregression', 'plotfit'}; % Train the Network [net,tr] = train(net,x,t); % Test the Network y = net(x); e = gsubtract(t,y); performance = perform(net,t,y) % Recalculate Training, Validation and Test Performance trainTargets = t .* tr.trainMask{1}; valTargets = t .* tr.valMask{1}; testTargets = t .* tr.testMask{1}; trainPerformance = perform(net,trainTargets,y) valPerformance = perform(net,valTargets,y) testPerformance = perform(net,testTargets,y) % View the Network view(net) if (false) genFunction(net,'myNeuralNetworkFunction'); y = myNeuralNetworkFunction(x); end if (false) genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes'); y = myNeuralNetworkFunction(x); end if (false) gensim(net); end 72 4.3. Results and Discussions This section presents the results and discussions of applying the proposed approach to seven cities: Kamloops, Kelowna, West Kelowna, Surrey, Vancouver, Vernon, and Victoria of BC. 4.3.1. Potential fire ignition source factors The investigation of the occurred fire incidents in the seven cities revealed that 40 ISFs-FIs, including mainly common and smart factors, categorized under eight attributes, are potential factors in SG-MURBs, as shown in Table 4-1 [18][32]. Four green factors under green building technologies were found to be causing fires, including tankless water heaters, cogeneration systems, battery storage systems, and PV roof panels [29]. Besides, relative frequencies of ISFs-FIs were determined and presented in Table 4-1 for the seven cities. The relative frequencies of the factors were found to be varying from one city to another. However, cooking equipment factor and smoker's material with an open flame factor were found to be the top two critical attributes in all the cities with relative frequencies (per year) of 4.84E-01 and 2.87E-01 in Kamloops, 5.42E-01 and 2.50E-01 in Kelowna, 3.48E-01 and 3.85E-01 in Surrey, 3.46E-01 and 3.54E-01 in Vancouver, 4.12E-01 and 3.24E-01 in Vernon, 3.20E-01 and 4.20E-01 in Victoria, and finally 1.20E-01 and 6.00E-01 in West Kelowna, respectively. Figure 4-4 shows stacked bars to compare relative frequencies of ignition sources of cities in BC. It was found that Kelowna and Kamloops have the highest relative frequency with 21% and 19%, respectively, for cooking equipment among the cities. Kamloops and Victoria have the highest percentages of fires due to heating equipment, with 26% and 25%, respectively. 73 Table 4-1: Relative frequencies (per year) of ISFs-FIs in seven cities Factors Ignition sources Kamloops Kelowna Surrey Vancouver Vernon Victoria West Kelowna COOKING EQUIPMENT (Attribute 1) 1 Stove, range, top burner area 4.59E-01 5.21E-01 3.02E-01 2.84E-01 3.82E-01 2.40E-01 8.00E-02 2 Deep fat fryer - separate appliance 0.00E+00 0.00E+00 1.15E-02 1.05E-02 0.00E+00 4.00E-02 0.00E+00 3 Open fired broiler, portable type - includes barbecue 0.00E+00 0.00E+00 1.38E-02 2.81E-02 0.00E+00 4.00E-02 0.00E+00 4 Toaster, waffle iron 8.20E-03 0.00E+00 4.61E-03 3.51E-03 0.00E+00 0.00E+00 0.00E+00 5 Cooking equipment - unclassified or unknown 1.64E-02 2.08E-02 1.61E-02 1.99E-02 2.94E-02 0.00E+00 4.00E-02 HEATING EQUIPMENT (Attribute 2) 6 Central heating unit - includes furnace, boiler, stoker 0.00E+00 2.08E-02 4.61E-03 7.02E-03 2.94E-02 2.00E-02 4.00E-02 7 Water heater 8.20E-03 0.00E+00 0.00E+00 4.68E-03 0.00E+00 0.00E+00 0.00E+00 8 Stationary space heater (includes wall furnace, unit heater, room heater, heating stove, baseboard heater) 2.46E-02 2.08E-02 4.61E-03 1.17E-03 0.00E+00 0.00E+00 0.00E+00 9 Portable space heater - includes room heater, salamander 8.20E-03 0.00E+00 0.00E+00 3.51E-03 0.00E+00 2.00E-02 0.00E+00 10 Fireplace, fireplace insert, chimney 8.20E-03 0.00E+00 0.00E+00 5.85E-03 0.00E+00 4.00E-02 0.00E+00 11 Heating equipment - unclassified or unknown 3.28E-02 0.00E+00 9.22E-03 3.51E-03 0.00E+00 0.00E+00 0.00E+00 APPLIANCES AND EQUIPMENT (Attribute 3) 12 Television, computer monitor 0.00E+00 0.00E+00 9.22E-03 9.36E-03 0.00E+00 0.00E+00 0.00E+00 13 Clothes dryer and washing machine 2.46E-02 6.25E-02 3.23E-02 2.81E-02 5.88E-02 2.00E-02 0.00E+00 14 Individual air conditioner, dehumidifier, or refrigeration unit 0.00E+00 0.00E+00 2.30E-03 4.68E-03 0.00E+00 0.00E+00 0.00E+00 15 Incinerator 8.20E-03 0.00E+00 0.00E+00 1.17E-03 0.00E+00 0.00E+00 0.00E+00 16 Appliances and equipment - unclassified or unknown 0.00E+00 0.00E+00 1.15E-02 1.52E-02 2.94E-02 0.00E+00 0.00E+00 ELECTRICAL DISTRIBUTION EQUIPMENT (Attribute 4) 17 Permanent electric wiring, cable (includes power line, junction box) 0.00E+00 2.08E-02 2.30E-03 7.02E-03 0.00E+00 0.00E+00 0.00E+00 18 Copper conductors and transformer 0.00E+00 0.00E+00 1.15E-02 1.99E-02 2.94E-02 0.00E+00 4.00E-02 19 Panel board, switchboard (includes fuse, circuit breaker) 8.20E-03 0.00E+00 2.30E-03 5.85E-03 2.94E-02 2.00E-02 0.00E+00 20 Switch, outlet (includes receptacle, socket) 8.20E-03 0.00E+00 2.30E-03 5.85E-03 0.00E+00 0.00E+00 0.00E+00 21 Temporary electric wiring and extension-appliance cords 8.20E-03 2.08E-02 3.23E-02 3.16E-02 0.00E+00 0.00E+00 4.00E-02 22 Electrical distribution equipment (unclassified or unknown) 8.20E-03 0.00E+00 9.22E-03 1.17E-02 0.00E+00 0.00E+00 0.00E+00 ELECTRICAL EQUIPMENT (Attribute 5) 23 Motor, < >1HP (includes motor forming an integral part of an appliance or separated by a belt, chain or connecting shaft, generator motor) 8.20E-03 2.08E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 24 Incandescent lamp, light bulb (includes lantern, flashlight) 0.00E+00 0.00E+00 2.07E-02 1.29E-02 5.88E-02 0.00E+00 4.00E-02 25 Fluorescent lamp (includes ballast) 8.20E-03 0.00E+00 2.30E-03 0.00E+00 0.00E+00 0.00E+00 4.00E-02 26 Electrical equipment - unclassified or unknown 0.00E+00 2.08E-02 1.15E-02 1.64E-02 2.94E-02 2.00E-02 0.00E+00 SMOKER'S MATERIAL AND OPEN FLAME (Attribute 6) 27 Smoker's material (includes cigarette, pipe, cigar and/or matches, lighter when used in conjunction with smoking) 1.31E-01 1.67E-01 2.30E-01 1.78E-01 1.18E-01 3.40E-01 2.80E-01 28 Match and lighter 0.00E+00 0.00E+00 6.91E-02 9.24E-02 8.82E-02 2.00E-02 1.20E-01 29 Lamp, lantern (excludes electric lamp, lanterns) 3.28E-02 2.08E-02 0.00E+00 1.17E-03 0.00E+00 0.00E+00 0.00E+00 30 Candle, taper 0.00E+00 0.00E+00 3.92E-02 3.86E-02 0.00E+00 4.00E-02 8.00E-02 31 Cutting torch and welding equipment and others (includes plumber's furnace, blow torch, plumber's torch, burner, soldering iron, paint stripping torch) 2.46E-02 2.08E-02 1.15E-02 1.75E-02 5.88E-02 0.00E+00 4.00E-02 32 Hot ashes, embers - not associated with smoker's material 1.64E-02 0.00E+00 9.22E-03 9.36E-03 2.94E-02 2.00E-02 4.00E-02 33 Smoker's material and "open" flames - unclassified or unknown 8.20E-02 4.17E-02 2.53E-02 1.75E-02 2.94E-02 0.00E+00 4.00E-02 EXPOSURE (Attribute 7) 34 Structure detached and outside storage container 3.28E-02 2.08E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 35 Exposure to open fire (includes campfire, bonfire, warning flare, rubbish fire, "open" incinerator) 0.00E+00 0.00E+00 1.15E-02 5.85E-03 0.00E+00 0.00E+00 4.00E-02 36 Exposure - unclassified or unknown 2.46E-02 0.00E+00 9.22E-03 1.05E-02 0.00E+00 4.00E-02 0.00E+00 MISCELLANEOUS (Attribute 8) 37 Heat treatment equipment (includes furnace, oven, quench tank, casting, molding, forging equipment) 0.00E+00 0.00E+00 2.30E-03 1.17E-03 0.00E+00 0.00E+00 0.00E+00 38 Fireworks (includes sparklers and flares) 0.00E+00 0.00E+00 2.07E-02 3.51E-03 0.00E+00 0.00E+00 0.00E+00 39 Chemical reaction, spontaneous combustion 0.00E+00 0.00E+00 2.30E-03 7.02E-03 0.00E+00 2.00E-02 0.00E+00 40 Miscellaneous igniting object - unclassified 8.20E-03 0.00E+00 5.07E-02 7.25E-02 0.00E+00 6.00E-02 4.00E-02 74 Vernon and Kelowna were found to be the cities recording the major fire incidents due to appliances and equipment with 28% and 20%, correspondingly. Similarly, it was found that Vancouver and West Kelowna have a high percentage of fires due to electrical distribution equipment, with 22% and 21%, respectively. Vernon and West Kelowna have a high percentage of BC fires due to electrical equipment, with 28% and 26%, correspondingly. West Kelowna and Victoria recorded a high percentage of fires due to the smoker's material and open flame with 23% and 16%, respectively. Figure 4-4: Relative frequencies of ISFs-FIs attributes for the seven cities, BC The related dollar losses to the ISFs-FIs in the seven cities were identified, as presented in the appendix (Table A-4-3). Remarkably, as presented in Figure 4-5, Vancouver and Surrey were found to be the cities recording the highest dollar loss with most of the eight attributes. It can be seen that the relative frequencies of the most potential ISFs-FIs and the related dollar loss vary significantly from one city to another. Consequently, fire prevention and protection strategies should be enhanced accordingly, such as avoiding or removing critical ISFs-FIs. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%COOKING EQUIPMENTHEATING EQUIPMENTAPPLIANCES AND EQUIPMENTELECTRICAL DISTRIBUTION EQUIPMENTELECTRICAL EQUIPMENTSMOKER'S MATERIAL AND OPEN FLAMEEXPOSUREMISCELLANEOUSRelative frequency ISFs-FIs AttributesKamloops Kelowna Surrey Vancouver Vernon Victoria West Kelowna 75 Figure 4-5: Dollar loss-percentages of ISFs-FIs attributes in the seven cities, BC 4.3.2. Benchmarking analysis and evaluation of ISFs-FIs The estimated relative frequency and dollar loss-based benchmarks for the 40 ISFs-FIs are presented in Table 4-2 and Table 4-3, respectively. The ISFs-FIs were fitted well in different PDFs, including Exponential (Expon) distribution, Extvalue, Levy, Pareto, Laplace, Uniform, and ExtvalueMin. The Exponential function was found to be the commonly fit PDF. The tables present the mean and maximum values of each fitted distribution of ISFs-FIs. The tables present five percentile values for each ISFs-FIs to present five different levels <=10% (VL), 10-25% (L), 25-50% (M), 50-75% (H), and >75% (VH), respectively. The presented results of the developed benchmarks effectively compare the ISFs-FIs among the cities, including identifying critical ISFs-FIs for each city, analyzing and comparing fire safety performance, and collaborating with other cities. The levels of relative frequency, total dollar loss, and risk-based levels of ISFs-FIs based on the developed benchmarks (Table 4-2 and Table 4-3) for the seven cities are presented in Table 4-4. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%COOKING EQUIPMENTHEATING EQUIPMENTAPPLIANCES AND EQUIPMENTELECTRICAL DISTRIBUTION EQUIPMENTELECTRICAL EQUIPMENTSMOKER'S MATERIAL AND OPEN FLAMEEXPOSUREMISCELLANEOUSDollar LossISFs-FIs Attributes Kamloops Kelowna Surrey Vancouver Vernon Victoria West Kelowna 76 Factors-Fr Function Mean Maximum <=10% (VL) 10-25% (L) 25-50% (M) 50-75% (H) >75% (VH) 1 RiskExtvalueMin(0.38996,0.11797) 3.21E-01 6.32E-01 1.23E-01 2.42E-01 3.46E-01 4.28E-01 4.45E-01 2 RiskExpon(0.0088639,RiskShift(-0.0012663)) 7.61E-03 8.15E-02 3.40E-04 1.27E-03 4.86E-03 1.10E-02 1.29E-02 3 RiskExpon(0.011699,RiskShift(-0.0016713)) 1.00E-02 8.20E-02 4.46E-04 1.69E-03 6.42E-03 1.45E-02 1.71E-02 4 RiskExpon(0.0023305,RiskShift(-0.00033293)) 2.00E-03 2.17E-02 8.86E-05 3.34E-04 1.27E-03 2.89E-03 3.41E-03 5 RiskLaplace(0.019883,0.011662) 1.98E-02 7.28E-02 6.55E-03 1.41E-02 1.98E-02 2.55E-02 2.74E-02 6 RiskUniform(0,0.046667) 2.00E-02 4.66E-02 1.34E-03 6.63E-03 1.99E-02 3.32E-02 3.59E-02 7 RiskExpon(0.0018393,RiskShift(-0.00026276)) 1.57E-03 1.33E-02 6.97E-05 2.64E-04 1.01E-03 2.28E-03 2.69E-03 8 RiskExpon(0.0073145,RiskShift(-0.0010449)) 6.26E-03 5.47E-02 2.81E-04 1.05E-03 4.02E-03 9.06E-03 1.07E-02 9 RiskExpon(0.0045294,RiskShift(-0.00064705)) 3.88E-03 3.93E-02 1.72E-04 6.53E-04 2.48E-03 5.61E-03 6.63E-03 10 RiskExpon(0.0077207,RiskShift(-0.001103)) 6.61E-03 5.28E-02 2.92E-04 1.11E-03 4.24E-03 9.57E-03 1.13E-02 11 RiskExpon(0.0065017,RiskShift(-0.00092882)) 5.56E-03 4.52E-02 2.46E-04 9.36E-04 3.57E-03 8.08E-03 9.51E-03 12 RiskExpon(0.0026533,RiskShift(-0.00037905)) 2.27E-03 2.23E-02 1.00E-04 3.81E-04 1.45E-03 3.29E-03 3.88E-03 13 RiskUniform(0,0.072917) 3.12E-02 7.29E-02 2.09E-03 1.03E-02 3.12E-02 5.20E-02 5.61E-02 14 RiskExpon(0.0009975,RiskShift(-0.0001425)) 8.54E-04 7.09E-03 3.77E-05 1.44E-04 5.48E-04 1.23E-03 1.46E-03 15 RiskExpon(0.001338,RiskShift(-0.00019115)) 1.14E-03 9.11E-03 5.06E-05 1.93E-04 7.34E-04 1.66E-03 1.96E-03 16 RiskExpon(0.0080196,RiskShift(-0.0011457)) 6.87E-03 6.31E-02 3.06E-04 1.15E-03 4.40E-03 9.95E-03 1.17E-02 17 RiskExpon(0.0043079,RiskShift(-0.00061541)) 3.69E-03 3.21E-02 1.65E-04 6.19E-04 2.36E-03 5.35E-03 6.29E-03 18 RiskExpon(0.014402,RiskShift(-0.0020575)) 1.23E-02 1.04E-01 5.42E-04 2.07E-03 7.91E-03 1.78E-02 2.10E-02 19 RiskLevy(0,0.00069999) 1.59E+00 1.09E+03 4.12E-05 3.11E-04 1.31E-03 6.65E-03 1.06E-02 20 RiskExpon(0.0023355,RiskShift(-0.00033365)) 2.00E-03 1.99E-02 9.00E-05 3.37E-04 1.28E-03 2.90E-03 3.42E-03 21 RiskUniform(0,0.046667) 2.00E-02 4.66E-02 1.37E-03 6.66E-03 1.99E-02 3.33E-02 3.59E-02 22 RiskExpon(0.0041585,RiskShift(-0.00059407)) 3.57E-03 4.02E-02 1.57E-04 6.00E-04 2.28E-03 5.15E-03 6.09E-03 23 RiskExpon(0.0041472,RiskShift(-0.00059245)) 3.55E-03 3.57E-02 1.58E-04 5.97E-04 2.27E-03 5.15E-03 6.06E-03 24 RiskExpon(0.018918,RiskShift(-0.0027026)) 1.61E-02 1.29E-01 7.29E-04 2.72E-03 1.03E-02 2.35E-02 2.76E-02 25 RiskExpon(0.0072144,RiskShift(-0.0010306)) 6.19E-03 6.42E-02 2.75E-04 1.03E-03 3.95E-03 8.95E-03 1.05E-02 26 RiskUniform(0,0.034314) 1.47E-02 3.42E-02 1.00E-03 4.88E-03 1.46E-02 2.44E-02 2.64E-02 27 RiskPareto(2.0171,0.11765) 2.32E-01 6.78E+00 1.23E-01 1.35E-01 1.65E-01 2.33E-01 2.60E-01 28 RiskUniform(0,0.14) 6.00E-02 1.40E-01 4.00E-03 1.98E-02 5.99E-02 9.99E-02 1.07E-01 29 RiskExpon(0.0078271,RiskShift(-0.0011182)) 6.70E-03 5.47E-02 2.97E-04 1.12E-03 4.29E-03 9.70E-03 1.14E-02 30 RiskExpon(0.028252,RiskShift(-0.0040361)) 2.42E-02 2.08E-01 1.06E-03 4.06E-03 1.55E-02 3.50E-02 4.13E-02 31 RiskExtvalue(0.016405,0.014402) 2.47E-02 1.46E-01 4.36E-03 1.16E-02 2.16E-02 3.43E-02 3.79E-02 32 RiskExtvalue(0.011803,0.010424) 1.78E-02 9.92E-02 3.09E-03 8.37E-03 1.56E-02 2.47E-02 2.73E-02 33 RiskExtvalue(0.022775,0.019186) 3.38E-02 1.60E-01 6.74E-03 1.65E-02 2.97E-02 4.66E-02 5.14E-02 34 RiskExpon(0.00766,RiskShift(-0.0010943)) 6.56E-03 6.38E-02 2.94E-04 1.10E-03 4.20E-03 9.50E-03 1.11E-02 35 RiskExpon(0.0081955,RiskShift(-0.0011708)) 7.02E-03 5.96E-02 3.15E-04 1.18E-03 4.50E-03 1.01E-02 1.19E-02 36 RiskExpon(0.012048,RiskShift(-0.0017211)) 1.03E-02 8.62E-02 4.62E-04 1.74E-03 6.61E-03 1.49E-02 1.76E-02 37 RiskExpon(0.00049625,RiskShift(-0.0000708926)) 4.24E-04 3.36E-03 1.86E-05 7.16E-05 2.73E-04 6.16E-04 7.25E-04 38 RiskExpon(0.0034637,RiskShift(-0.00049482)) 2.96E-03 2.77E-02 1.32E-04 4.97E-04 1.89E-03 4.30E-03 5.06E-03 39 RiskExpon(0.0041888,RiskShift(-0.0005984)) 3.58E-03 2.84E-02 1.60E-04 6.06E-04 2.30E-03 5.20E-03 6.13E-03 40 RiskUniform(0,0.0846) 3.62E-02 8.45E-02 2.48E-03 1.20E-02 3.62E-02 6.03E-02 6.51E-02 Table 4-2: Best fitted distributions for relative frequencies of ISFs-FIs 77 Table 4-3: Best fitted distributions for total dollar loss of ISFs-FIs Factors-TDL Function Mean Maximum <=10% (VL) 10-25% (L) 25-50% (M) 50-75% (H) >75% (VH) 1 RiskLevy(0,139509.9879) $67,998,456,464.24 $67,912,723,070,784.40 $20,691.91 $74,601.59 $275,443.86 $1,339,386.01 $2,136,094.61 2 RiskExpon(54328,RiskShift(-7761.2)) $46,524.11 $376,931.63 $2,048.74 $7,807.28 $29,875.23 $67,526.52 $79,462.24 3 RiskExpon(10083,RiskShift(-1440.5)) $8,642.87 $78,087.85 $383.08 $1,457.86 $5,536.10 $12,511.61 $14,760.44 4 RiskExpon(271.43,RiskShift(-38.776)) $232.62 $2,060.41 $10.38 $39.17 $149.02 $336.47 $397.45 5 RiskLevy(0,51.4427) $154,320.70 $113,869,793.35 $3.04 $22.77 $96.92 $488.66 $784.32 6 RiskLevy(0,1897.3079) $4,577,381.51 $3,672,051,452.14 $185.75 $919.77 $3,645.58 $18,115.86 $28,914.55 7 RiskExpon(11057,RiskShift(-1579.6)) $9,473.04 $84,129.69 $423.65 $1,594.29 $6,070.63 $13,738.72 $16,167.74 8 RiskExpon(31028,RiskShift(-4432.7)) $26,642.20 $291,086.93 $1,169.69 $4,464.01 $17,042.30 $38,538.97 $45,459.87 9 RiskExpon(12942,RiskShift(-1849)) $11,103.01 $112,149.43 $498.25 $1,867.55 $7,106.86 $16,044.93 $18,948.08 10 RiskExpon(22942,RiskShift(-3277.6)) $19,691.32 $208,594.72 $867.85 $3,296.32 $12,587.10 $28,464.43 $33,570.07 11 RiskExpon(10164,RiskShift(-1452)) $8,753.27 $120,093.93 $383.17 $1,467.35 $5,588.03 $12,636.58 $14,885.41 12 RiskExpon(47250,RiskShift(-6750)) $40,453.25 $334,446.86 $1,785.16 $6,828.09 $25,944.78 $58,583.71 $69,081.76 13 RiskLevy(0,4867.8521) $76,051,848,242.65 $76,048,281,067,014.90 $667.27 $2,547.87 $9,545.79 $46,672.92 $74,668.24 14 RiskExpon(20814,RiskShift(-2973.5)) $17,848.89 $164,341.13 $800.34 $2,996.71 $11,451.32 $25,854.18 $30,517.51 15 RiskExpon(0.28571,RiskShift(-0.040816)) $0.25 $2.56 $0.01 $0.04 $0.16 $0.35 $0.42 16 RiskExpon(41842,RiskShift(-5977.6)) $35,896.30 $351,848.65 $1,585.76 $6,038.29 $22,944.52 $51,906.12 $61,308.05 17 RiskExpon(222142,RiskShift(-31734)) $190,238.26 $1,555,304.53 $8,498.79 $32,071.57 $121,836.33 $275,988.62 $325,731.98 18 RiskExpon(218828,RiskShift(-31261)) $187,493.32 $1,603,262.42 $8,234.64 $31,669.93 $120,295.39 $271,717.03 $319,872.77 19 RiskLevy(0,245.7351) $494,609.02 $329,020,224.30 $14.65 $108.78 $462.82 $2,326.62 $3,749.91 20 RiskExpon(67077,RiskShift(-9582.6)) $57,454.67 $470,821.82 $2,587.40 $9,666.20 $36,888.30 $83,188.16 $98,225.13 21 RiskLevy(0,819.6008) $6,965,600.26 $6,358,028,271.94 $48.63 $363.32 $1,544.88 $7,755.56 $12,431.30 22 RiskExpon(54357,RiskShift(-7765.3)) $46,662.76 $497,981.57 $2,053.44 $7,847.33 $29,857.79 $67,534.33 $79,490.34 23 RiskExpon(357.14,RiskShift(-51.02)) $305.86 $2,699.81 $13.74 $51.27 $196.17 $442.71 $522.85 24 RiskExpon(43271,RiskShift(-6181.6)) $37,045.98 $299,597.42 $1,666.09 $6,242.62 $23,784.13 $53,634.68 $63,383.69 25 RiskExpon(17928,RiskShift(-2561.2)) $15,367.47 $133,881.93 $688.79 $2,594.85 $9,833.93 $22,270.56 $26,264.62 26 RiskExpon(140835,RiskShift(-20119)) $120,806.93 $1,180,318.38 $5,365.82 $20,218.97 $77,476.57 $174,614.12 $206,448.25 27 RiskUniform(0,2231748) $957,513.26 $2,231,079.27 $63,484.64 $318,890.64 $955,853.04 $1,592,807.62 $1,721,372.60 28 RiskLevy(0,2080.9202) $4,063,571.18 $2,360,733,259.97 $122.78 $925.48 $3,925.58 $19,777.90 $31,724.11 29 RiskExpon(24928,RiskShift(-3561.2)) $21,346.47 $178,908.49 $948.97 $3,607.87 $13,703.58 $30,975.43 $36,523.94 30 RiskExpon(191840,RiskShift(-27405)) $164,523.06 $1,478,854.84 $7,393.16 $27,764.11 $105,545.19 $237,783.47 $281,104.68 31 RiskLevy(0,7959.8198) $24,921,253.58 $20,539,569,754.71 $1,083.98 $4,154.58 $15,600.72 $76,149.21 $121,111.70 32 RiskExpon(7035.7,RiskShift(-1005.1)) $6,023.88 $49,357.20 $266.86 $1,018.46 $3,867.70 $8,729.86 $10,309.15 33 RiskLevy(0,592.2345) $1,007,677.53 $695,211,703.79 $35.10 $262.29 $1,115.12 $5,641.01 $9,042.87 34 RiskExpon(118064,RiskShift(-16866)) $101,131.15 $812,004.06 $4,489.26 $17,076.05 $64,750.16 $146,609.37 $172,686.43 35 RiskExpon(800,RiskShift(-114.29)) $686.08 $6,809.61 $30.33 $115.56 $439.20 $992.36 $1,172.15 36 RiskExpon(124371,RiskShift(-17767)) $106,481.25 $854,268.79 $4,762.64 $18,009.34 $68,209.15 $154,518.26 $181,833.71 37 RiskExpon(25571,RiskShift(-3653.1)) $21,899.89 $178,809.71 $963.33 $3,687.41 $14,030.06 $31,766.77 $37,498.51 38 RiskExpon(11408,RiskShift(-1629.8)) $9,769.49 $80,806.64 $440.23 $1,641.55 $6,258.21 $14,145.18 $16,694.34 39 RiskExpon(5671.4,RiskShift(-810.2)) $4,858.33 $40,036.56 $212.85 $814.38 $3,118.21 $7,039.14 $8,290.02 40 RiskLevy(0,42.6753) $75,065.42 $47,599,211.40 $2.52 $19.01 $80.19 $405.76 $651.20 78 Factors Kamloops Kelowna Surrey Vancouver Vernon Victoria West Kelowna Fr(i) TDL(i) R(i) Fr(i) TDL(i) R(i) Fr(i) TDL(i) R(i) Fr(i) TDL(i) R(i) Fr(i) TDL(i) R(i) Fr(i) TDL(i) R(i) Fr(i) TDL(i) R(i) 1 VH M H VH H VH M VH H M VH H H M M L M L VL VL VL 2 L L VL L L VL VH VH VH H VH VH L L VL VH L M L L VL 3 L L VL L L VL H H H VH VH VH L L VL VH L M L L VL 4 VH L M L L VL VH H VH VH VH VH L L VL L L VL L L VL 5 M VH H H VL VL M VH H H VH VH VH H VH VL VL VL VH VH VH 6 L VL VL H VH VH L VH M M VH H H H H H L L VH H VH 7 VH L M L L VL L L VL VH VH VH L L VL L L VL L L VL 8 VH M H VH L M H L L M VH H L L VL L L VL L L VL 9 VH L M L L VL L L VL H VH VH L L VL VH M H L L VL 10 H L L L L VL L L VL H VH VH L L VL VH L M L L VL 11 VH VH VH L L VL VH M H M VH H L L VL L L VL L L VL 12 L L VL L L VL VH VH VH VH VH VH L L VL L L VL L L VL 13 M H M VH H VH H H H M VH H VH M H M M L L VL VL 14 L L VL L L VL VH H VH VH VH VH L L VL L L VL L L VL 15 VH L M L L VL L VH M H VH VH L L VL L L VL L L VL 16 L L VL L L VL VH VH VH VH VH VH VH L M L L VL L L VL 17 L L VL VH VH VH M L L VH M H L L VL L L VL L L VL 18 L L VL L L VL H VH VH VH VH VH VH L M L L VL VH L M 19 VH VH VH VL VL VL H H H H VH VH VH VH VH VH VH VH VL VL VL 20 VH VH VH L L VL H L L VH M H L L VL L L VL L L VL 21 M M L H VH VH H VH VH H VH VH L VL VL L VL VL VH VH VH 22 VH L M L L VL VH VH VH VH VH VH L L VL L L VL L L VL 23 VH VH VH VH VH VH L L VL L L VL L L VL L L VL L L VL 24 L L VL L L VL H VH VH H VH VH VH L M L L VL VH L M 25 H L L L L VL M L L L L VL L L VL L L VL VH VH VH 26 L L VL H L L M H M H VH VH VH L M H L L L L VL 27 L M L H VH VH H VH VH H H H VL M VL VH L M VH L M 28 L VL VL L VL VL H VH VH H VH VH H VH VH M H M VH H VH 29 VH VH VH VH VH VH L L VL M L L L L VL L L VL L L VL 30 L L VL L L VL VH VH VH VH VH VH L L VL VH L M VH H VH 31 H M M M VH H L VH M M VH H VH L M VL VL VL VH M H 32 H VH VH VL L VL M H M M VH H VH L M H L L VH L M 33 VH VH VH H H H M VH H M VH H M H M VL VL VL H VL VL 34 VH VH VH VH L M L L VL L L VL L L VL L L VL L L VL 35 L L VL L L VL VH VH VH H L L L L VL L L VL VH L M 36 VH M H L L VL H VH VH H VH VH L L VL VH H VH L L VL 37 L L VL L L VL VH M H VH VH VH L L VL L L VL L L VL 38 L L VL L L VL VH VH VH H L L L L VL L L VL L L VL 39 L L VL L L VL H VH VH VH VH VH L L VL VH M H L L VL 40 L H L L VL VL H VH VH VH VH VH L VL VL H VH VH H VH VH Table 4-4: Levels of relative frequency, total dollar loss, and risk-based levels of ISFs-FIs for seven cities 79 The levels of ISFs-FIs vary from one city to another. Furthermore, critical ISFs-FIs were identified to help each city develop ignition-specific fire safety strategies. Remarkably, 50% of the ISFs-FIs under green features were found to have high-risk levels to cause a fire, including PV roof panels and cogeneration systems. Whereas the rest, 50%, was found to have a medium level, including tankless water heaters and battery storage systems [20]. Table 4-4 and Figure 4-6 show the most critical ISFs-FIs, under VH and H risk levels, for each city. Kamloops was found to have 20% of the ISFs-FIs with VH-risk level, and 10% (H). Kelowna was found to have 20% (VH) and 5% (H), whereas Surrey registered 43% (VH) and 20% (H). Vancouver registered 58% (VH) and 28% (H). Vernon and Victoria have the same percentages for VH and H and 8% and 5%, respectively. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%KamloopsKelownaSurreyVancouverVernonVictoriaWest KelownaRisk LevelCitiesVL L M H VH Figure 4-6: Proportions of risk levels in the seven cities, BC 80 Kamloops was found to have 20% of the ISFs-FIs with VH-risk level, and 10% (H). Kelowna was found to have 20% (VH) and 5% (H), whereas Surrey registered 43% (VH) and 20% (H). Vancouver registered 58% (VH) and 28% (H). Vernon and Victoria have the same percentages for VH and H and 8% and 5%, respectively. Lastly, West-Kelowna recorded 18% (VH), and 3% of ISFs-FIs were found to have H risk levels. It can be noticed that Vancouver and Surrey are the most critical cities in BC. Consequently, these results help decision-makers enhance fire prevention strategies at different scales, cities, and provincial, accordingly. 4.3.3. Correlation and association analysis The analysis of 4447 fire incidents revealed that the predictors: age group categories (AC), sex of casualty (SE), contributing condition (CC), ignition of clothing or other fabrics (ICOF), and type of fabric or material that might be ignited (TFMI) have a high association with specific types of the responses, including probable/possible consequence (PC), cause failure escape (CFE), and nature of casualties (NC). Figure 4-7 presents that 79% of the fires are associated with adults (18 – 64 years); 13% are related with senior citizens (65 years of age and older), 4% with youth (12 to 17 years of age), and 4% children (11 years and under). Regarding the response of Probable Consequence, 38% of the fire incidents caused smoke inhalation, 37% burn, and 6% physical injury. Regarding the nature of casualties, 46% of the casualties have a minor injury (less than a 1-day hospital or off work), 27% of the fire incidents caused a light injury (hospitalized 1-2 days and/or off work 1-15 days), 12% caused a serious injury (hospitalized 3+ days and/or off work 16+ days), and 15% of the fire incidents caused death. 81 Note: AC: age group categories, PC: probable/possible consequence, and NC: nature of casualties. It can be seen that specific factors of the predictors were associated with particular types of responses; adults (18 – 64 years) have an association of 30% of the fire incidents with a burn, whereas 29% were associated with smoke inhalation. Furthermore, the adults were found to be associated with minor injury (39%), light injury (21%), serious injury (9%) and death (11%). Moreover, the correlation analysis, using the Spearman correlation, was found to be highly correlated and statistically significant with a p-value < 0.005. 2422079 26 691284134326212850776 85122 20 111384 1 201/11/21/31/41/92/12/22/32/42/93/13/23/33/43/94/14/24/34/44/9COUNTAC/PC1801791247047217179334011 14 936127 32 7948151/11/21/31/42/12/22/32/42/53/13/23/33/44/14/24/34/4AC/NCFigure 4-7: Pareto results of the predictor AC, the responses PC and NC, and their association 82 Note: SE: sex of casualty, PC: probable/possible consequence, CFE: cause failure escape, and NC: nature of casualties. This indicates that SG-MURBs with residents of specific predictors could lead to specific responses. Besides, it helps to prepare and improve fire prevention strategies accordingly. Figure 4-8 shows that males were involved in 68% of recorded fires, whereas females were involved in 31% of the fire incidents. The results showed that males were associated with burns with 28% and smoke inhalation with 24%. Whereas females registered 15% with smoke inhalation and just 10% with burn impact. Male and females were found to be associated with the cause of failure to escape. Males were associated with 33% of the minor injury,17% of the light injury, 10% of death, and 8% of serious injury. Whereas females were associated with all 1045 123923212636866843255312272 211/11/21/31/41/92/12/22/32/42/93/23/9COUNTSE/PC7292171349415 3631 114140845711488385 16 236411 221/501/511/521/531/541/551/571/581/592/502/512/522/532/552/572/582/593/563/58COUNTSE/CFE456144574836012426034151532031/11/21/31/41/52/12/22/32/43/23/3SE/NCFigure 4-8: Pareto results of the predictor SE and its association with the responses PC, NC, and CFE 83 types of nature of casualties with different percentages, 14% minor injury, 9% light injury, and 5% death. It can be noticed that the fire impact percentage on a male is almost double that of the female; this helps in selecting the audience to increase the awareness of fire impacts to enhance prevention and protection strategies. 4.3.4. Classification results 4.3.4.1. Fire origin-based classification Fire origin-based classification results were determined using different algorithms, as presented in Figure 4-9. The results found that ensemble boosted trees and medium trees were the best classifier algorithms with performance accuracy of 63% and 62%, respectively. Linear discriminant and cubic SVM algorithms had the lowest accuracy, with 55% and 57%, respectively. The confusion matrix showed that the ensemble boosted trees algorithm classifies only fires in the function area well with a performance ability of 83%, presented in the intersection cell of 2nd column and 2nd row, to classify fires in the function area. The other factors (17%) in the second row were misclassified: 5% of the factors were incorrectly classified as a fire occurs at means of egress and assembly area, 1% were classified as a fire occurs at storage area – inside a building, 1% were classified as a fire occurs at service & equipment area, 3% were classified as a fire occurs at structural area, and 7% were classified as a fire occurs at outside area. 84 Figure 4-9: Performance of classifiers with confusion matrix and ROC curve of ensemble boosted trees classifier Figure 4-9 shows the results of the ROC curve with the FPR and TPR values for the ensemble boosted trees classifier. The figure also presents the classifier's overall quality through the area under the ROC curve; the function area has the highest value of 0.83 of the AUC, Area under 61.2 62.459.954.959.459.757.159.158.7 59.458.75859.258.258.6 59.5636258.21: Means of egress and assembly area 2: Function area 4: Storage area – inside building 5: Service facilities 6: Service & equipment area 7: Structural area 9: Outside area 85 the ROC Curve, providing an aggregate measure of performance across all possible classification thresholds. 4.3.4.2. Nature of casualties-based classification Figure 4-10 presents the performance results of the nature of casualties-based classification using different algorithms. The results found that a coarse tree is the best algorithm among the classifiers, with a performance accuracy of 50.9%. Fine KNN was found to be having the lowest accuracy with 45%. Figure 4-10: Performance of nature of casualties-based classifiers with confusion matrix and ROC curve of a coarse tree classifier 50.750.850.950.349.3 50.150.249.344.949 49.648.948.9 49.550.850.21: Death 2: Minor injury 3: Light injury 4: Serious injury 86 The confusion matrix shows that the coarse tree algorithm classifies only minor injuries well with a performance ability of 89%, presented in the intersection cell of 2nd column and 2nd row, to classify the nature of casualties. The other factors (11%) in the second row were misclassified: 6% of the factors were incorrectly classified as death, and 5% were classified as serious injury. The ROC curve with the FPR and TPR values for the coarse tree classifier, as the figure shows, discovered the overall quality of the classifier. Death prediction with an AUC of 74% is the highest prediction ability. 4.3.5. Artificial Neural Network results The results of the ANN were determined using 30 neurons with three algorithms. The BR algorithm has a slight preference in terms of prediction accuracy, followed by LM and SCG algorithms, accordingly. The BR algorithm-based ANN model has an R2 of 99 % in predicting possible fire impacts based on the defined predictors. Figure 4-11 shows the training results of 3112 fire incidents with 1000 iterations for the BR algorithm that stops generalization improvement at R of 0.99439. The testing results of 667 fire incidents found that R is 0.99426, whereas the total R-value is 0.99437. The error histogram with 20 bins shows that most of the datasets have a low error. The R values of training, validation, and testing and the all-R value for the LM algorithm were found to be equal to 0.99438, 0.99375, 0.99426, and 0.99426, respectively. The all-R value for the SCG algorithm was found to be equal to 0.99403. The MSE of the model was found to have 6.98 at epoch 669. MATLAB function, as presented, was generated to produce results and solve similar problems. Additionally, the ANN simulink diagram is generated, as presented in Figure 4.12, to simulate ANN in Simulink or deploy with Simulink Coder tools. The Simulink diagram was developed as an efficient technique to visualize the possible scenarios of the responses. 87 Figure 4-11: Results of the BR-based ANN for prediction of fire impacts 88 Note: AC: age group categories, SE: sex of casualty, CC: Contributing Condition, ICOF: ignition of clothing or other fabrics, TFMI: type of fabric or material that might be ignited, PC: probable/possible consequence, CFE: cause failure escape, and NC: nature of casualties. Figure 4-12: Results of the ANN-based Simulink model for fire impacts MATLAB Function for fire impacts prediction function [Y,Xf,Af] = myNeuralNetworkFunction(X,~,~) %MYNEURALNETWORKFUNCTION neural network simulation function. % [Y] = myNeuralNetworkFunction(X,~,~) takes these arguments: % X = 1xTS cell, 1 inputs over TS timesteps % Each X{1,ts} = 5xQ matrix, input #1 at timestep ts. % and returns: % Y = 1xTS cell of 1 outputs over TS timesteps. % Each Y{1,ts} = 3xQ matrix, output #1 at timestep ts. % where Q is number of samples (or series) and TS is the number of timesteps. %#ok<*RPMT0> % ===== NEURAL NETWORK CONSTANTS ===== % Input 1 x1_step1.xoffset = [1;1;10;9;9]; x1_step1.gain = [0.666666666666667;1;0.222222222222222;0.0666666666666667;0.05]; x1_step1.ymin = -1; % Layer 1 b1 = [-0.19208421166161482319;0.42987343214565526539;-0.72816317014893217063;-0.0047239622781728649037;0.41045355720756460105;1.4529894862608203621;1.1609003558411417067;-0.90633415609373291222;-0.4813203587647099102;-0.97917249280073070938;-0.93853608611376293513;0.4112639037681311005;1.137907849024714535;-0.68885076503112641078;-0.033383902289897793292;0.77292501410426928121;-0.14792404932176261845;-0.56979374121053616431;-0.44869177766477297276;-0.32411068466862952286;-0.14779608936191240831;1.0169132247658676516;-1.2029078539001303305;2.4728544071468956389;-0.82973203343269863463;-0.58071050861877859806;-1.0087465911185995449;-0.83715086818748041253;0.69931549096920087027;0.76324354835853436274]; IW1_1 = [-0.55641267466224131333 -0.56919576527722071102 0.044808663190155653688 -0.058502520622602506573 0.064301216464252311522;0.2327889073196938996 1.4032782682634175053 -0.11202571713917031104 -1.3995496602731392066 2.4959710265760595149;-1.0682819607222304725 -0.4075031334575566766 89 0.47782278479691314699 -1.5628864602799605876 1.5727799231880155162;0.012891385799954224387 1.3283884980524258701 0.35941523133883940266 -0.6032638094339910495 0.47272379593830465838;-0.10687991236900185044 0.9440094414262522049 -1.6777558283794069549 2.2902140372570976012 -1.9633603798992735889;-0.41296960246295688624 0.52152469668565581973 1.6891099932421294039 -1.6308984380519846447 1.9661765437860827532;0.0050511930147903085636 0.046256793603019125127 0.87593439563576436235 -4.5290320537249630917 1.6284391039621883834;-0.3595390787255251519 -0.78378868869899798177 -2.5787919825167660015 -0.52091386099362968487 0.29097414449799280378;0.75362680743013454876 -0.27803528666169785577 -0.46458188643746600732 -0.48852468130866411045 0.0041934836562227866333;-1.1941477422948640896 0.22277337868657492992 0.22552672940009255154 3.6625983734474871767 -0.067643336709712550969;-0.56113856118222027458 -0.44287720443690786754 -0.86387325583566632048 -0.11701351013525954081 0.65097303287885266432;-0.12806543712975226867 -0.10189044255662683958 2.6450086346721359831 -0.42780884203978719604 2.1705986001905945848;0.47604080054557001977 -0.17659721438254652459 -1.9816447319415380068 1.0449534652229981457 0.97740148156152828474;-1.0592270650622883643 0.25667094305092391648 0.27955911802310245973 -0.57160235153646277162 -0.98157279002610287044;1.0850865113904808457 0.50220403936726154548 0.42840514598720347283 0.51076099759015425317 1.1092536172049276555;-1.1897361611961547734 0.83777976508467200123 -0.038081691358950481296 -2.7470383496532839729 -0.10773594600261207632;-0.16017354928926078039 0.073143673300525166581 -0.053485053802165956338 -0.376119786740260309 -0.0080857876806290206878;0.84277439555989130948 0.90934912934921330319 -1.3044836419986922671 -0.21276941664727824777 1.3721640589103472063;-0.66697851132676932995 0.027545937599101122384 1.811838586490491565 -0.7921080071860894245 -0.56265265184799528875;0.58765513298342375581 0.57454009718858778388 -1.7783143584412866023 0.97914372493973378386 -0.72279040090737867175;-0.16009113257460100277 0.073188140343801458276 -0.053556062462289735771 -0.37636299336861955833 -0.007830150522219275766;-0.70409163787448969529 -1.2766511431704510304 0.20002287067228066708 -0.53094203585185428818 -1.3285729907400845029;1.0668287352232128651 -0.21479884431651144472 0.03081575764679149454 0.14210977411196976106 1.1674414969147464127;0.70757935091206425327 -0.88950005881241855921 -1.3474250619227912118 -0.98775593862002830647 -0.86057365391745521688;-0.17644422080723223822 0.3388520152534219565 -1.529935210104300447 0.042185523086184589792 -0.68813905167855293765;0.091537708482789248432 0.141892529292831443 1.6658136328683832428 1.3563445048241711266 -0.17349778549155600582;-0.82557006672409549708 0.88128163004994508611 2.3098853369501815003 1.0360666452747442356 1.4618674634088191233;-0.40296836697880333844 -0.077283672936758390781 -3.8510524222785611492 -0.69747785652113081234 0.96836827778189260574;1.4389530384679956043 0.16430250509997235131 0.13811481116598814389 1.0701755664347043506 -0.36028111262309342466;1.6539551335363020801 0.29480374678708454184 -1.1527866803957380615 1.0687997651738903038 -0.40566467785353299558]; % Layer 2 b2 = [0.54404764705147456993;0.20610656018798081957;-0.34562920907329736098]; LW2_1 = [-0.74265560306470190799 0.22811624581903147679 0.5830478753947082593 -1.1478258788711008798 0.28450636279773172665 -0.85272523847989856183 -0.15809472333841045888 -0.23936703073867363067 -0.43147816814741285496 -0.29895850255428857434 0.27773856783133560322 1.0223832155729566384 -1.7508724233933794512 -1.2889308600191489163 -0.83254584608937376444 0.86521795501169107556 -0.40894358427759153196 -0.20199192677131805329 -0.45078645238518932903 -0.40610346171380412583 -0.4088950532420202455 -0.44608976447094267348 0.25007573598794075043 -0.52317437779872888459 1.5060428887364774031 -0.78069657027872818578 0.14295259411289082396 -0.40644518040968868888 1.2306495153699577028 -0.24914585268221642322;-0.20368165131541460244 -1.2123204878600173373 -1.5004174692695144699 0.35206552299813625995 1.9089816730256690125 2.7892556772036321 2.0658921699824253082 1.7485075601350945629 0.90878623735883834467 2.2796480904481701835 -1.0799146409972315563 -1.4207271245247152791 -0.26257503514187585658 0.82446334051109948504 -0.23636951639859898888 -1.6892237025796323913 0.045269455707435367342 1.3648897290097115054 -1.4516410047285299711 -1.5833669345578180732 0.045394502946636751395 1.5875394659936172825 1.6382596862199352294 -2.2480324693335536068 0.50902812164124278116 -1.693697812669869851 1.3145438319875419886 -1.5510384540975441503 0.17232110140872838877 -1.1673294188492273626;-0.14741064373594373049 -0.023822588818364267421 -0.511511722157417692 0.25415856991513630092 -0.013344865244146995181 0.56053746121309244721 0.13918970086093521155 0.19142533620121765514 0.54134921084615794662 0.48554041270132869412 0.23876686539240007945 -0.56091222703245124936 -0.16693994328986871967 0.43110851128171889801 0.29271774485895346585 -0.45309097561336314497 0.40466712645744273846 0.12437772953297317191 -0.29130641009773100558 -0.27983376623151057228 0.40495714093798690358 0.35982143189155008089 -0.05073622533781805416 0.10751344946876491282 -0.29774779124728406599 -0.22141706651704970432 0.23908747705698890629 -0.29888548936560560554 -0.092389560838741099924 -0.24495315210344853485]; % Output 1 y1_step1.ymin = -1; y1_step1.gain = [0.25;0.222222222222222;0.5]; y1_step1.xoffset = [1;50;1]; % ===== SIMULATION ======== % Format Input Arguments isCellX = iscell(X); if ~isCellX X = {X}; end 90 % Dimensions TS = size(X,2); % timesteps if ~isempty(X) Q = size(X{1},2); % samples/series else Q = 0; end % Allocate Outputs Y = cell(1,TS); % Time loop for ts=1:TS % Input 1 Xp1 = mapminmax_apply(X{1,ts},x1_step1); % Layer 1 a1 = tansig_apply(repmat(b1,1,Q) + IW1_1*Xp1); % Layer 2 a2 = repmat(b2,1,Q) + LW2_1*a1; % Output 1 Y{1,ts} = mapminmax_reverse(a2,y1_step1); end % Final Delay States Xf = cell(1,0); Af = cell(2,0); % Format Output Arguments if ~isCellX Y = cell2mat(Y); end end % ===== MODULE FUNCTIONS ======== % Map Minimum and Maximum Input Processing Function function y = mapminmax_apply(x,settings) y = bsxfun(@minus,x,settings.xoffset); y = bsxfun(@times,y,settings.gain); y = bsxfun(@plus,y,settings.ymin); end % Sigmoid Symmetric Transfer Function function a = tansig_apply(n,~) a = 2 ./ (1 + exp(-2*n)) - 1; end % Map Minimum and Maximum Output Reverse-Processing Function function x = mapminmax_reverse(y,settings) x = bsxfun(@minus,y,settings.ymin); x = bsxfun(@rdivide,x,settings.gain); x = bsxfun(@plus,x,settings.xoffset); end With respect to the first research question, it was found that SG-MURBs have 40 potential ignition source factors classified under eight main attributes. This investigation is comprehensive and the first of its kind to help define preventive fire safety strategies for specific factors. Moreover, smart features were found to be having the ability to cause fires [433], especially with the control-room in SG-MURBs. However, considering the features with prevention and protection strategies could reduce fire relative frequencies, fire extension, 91 fire damages, and minimize dollar loss; especially as more than 70% of the fires in SG-MURBs of the seven cities were found to be related to equipment and appliances. This supports the claim of CURRAN [434], where smart devices help to double-check the equipment effectively; besides, smart features help to get alerts as reminders and for dangerous-cases aiding to enhance fire prevention and protection. Furthermore, four green features were causing fires, including tankless water heaters, cogeneration systems, battery storage systems, and PV roof panels. This investigation supports fire and building codes, including Fire Code Canada and Building Code of Australia, where all sources of ignition shall be eliminated [435]; investigating all sources of ignition is essential to enhance safety measures [436]. Furthermore, this helps to strengthen prevention activities to control all possible fire hazards [437]. In addition, this investigation supports the International Fire Code that requires ignition sources to be maintained in an approved manner [292]. The second research question in this study sought to determine the most critical ISFs-FIs in SG-MURBs. The developed benchmarks helped determine the most critical ISFs-FIs quantitatively, helping the decision-makers prioritize and take immediate actions accordingly at different scales, including the city and provincial levels. The defined critical factors help define the ISFs-FIs that should be avoided, minimized, or transferred to other parties, such as insurance companies. Furthermore, it helps decision-makers install and invest in the appropriate fire prevention and protection safeguards, supporting the International Fire Code [298], especially with smart and green features in buildings. Preventive strategies could involve designing, including the isolation of ignition sources from the combustible materials, training, and education to increase community awareness about fire prevention techniques and the critical ignition source factors in SG-MURBs. 92 Regarding the third question, according to the results, installing appropriate suppression materials in a specific fire location in SG-MURBs could effectively and efficiently control fire impacts. Whereas investing in the identified critical ignition sources helps to enhance fire prevention strategies and reduce dollar loss correlated to specific factors and attributes for each city. According to the best-fitted distribution-based mean values of total dollar loss in BC, improving fire prevention and protection strategies of a stove, range, top burner area, clothes dryer, and washing machine could reduce the total dollar loss to more than 90%. Even though improving prevention strategies requires investment, this investment has many benefits, including having lower treatment costs, less suffering, protecting people's lives psychologically and physically, and avoiding the indirect cost (stress, sickness, interruption from work, etc.) and pollution. The total dollar loss-based critical factors varied from one city to another; in Victoria, an efficient fire control of stove, range, top burner area, and smoker's material reduces total dollar loss to 66%. Concerning the fourth research question, fire impacts in SG-MURBs were associated, statistically significant, with fire predictors, including age, sex, condition, ignition of clothing, and type of fabric. Furthermore, specific factors of the predictors were found to be well correlated with factors of the responses; knowing age, sex, and condition of the residents with the type of combustible materials in SG-MURBs leads to an effective prediction of fire impacts. Consequently, the ANN model was developed with a determination coefficient of 99%. Therefore, prevention strategies should be improved accordingly. MATLAB function was generated to help predict fire impact based on specific predictors effectively. Moreover, the Simulink model was established to visualize and analyze the possible scenarios of fire impacts efficiently. 93 4.4. Summary This study developed an approach to assess the potential ignition sources and their financial impacts in SG-MURBs. The developed approach was applied to seven cities in BC. The results found that forty ISFs-FIs, categorized under eight attributes, to be potential factors in SG-MURBs. Cooking equipment factor and smoker's material and open flame factors were the top two attributes with high relative frequencies in all the cities. The related dollar loss to the ISFi-FIs was identified, and remarkably, Vancouver and Surrey were the cities recording the highest dollar loss with most of the ignition attributes. It was found that the relative frequencies of the most potential ISFs-FIs and the related dollar loss vary significantly from one city to another. Consequently, fire prevention and protection strategies should be enhanced accordingly, such as avoiding or removing critical ISFs-FIs and installing the appropriate fire suppression systems. Focusing on ISFs-FIs with critical risk level, Kamloops had 20% of the ISFs-FIs with VH risk level, Kelowna also recorded 20%, Surrey registered 43%, Vancouver recorded 58%, and West-Kelowna had 18%; whereas Vernon and Victoria were found to have 8% for both cities. It was found that the functioning area had the highest fire incidents, whereas 65% of these fires occurred in kitchens. This analysis improves prevention strategies and installs extinguishing systems in the appropriate place for effective and efficient fire suppression. The results of all the algorithm-based ANN models were found to be the best machine learning method for fire-related predictions, having the ability to predict fire impacts very accurately with an R2 of 99%. Consequently, ANN. Fire prevention strategies can be improved accordingly, including installing and using the ignition of sources, education and awareness of residents, and installing suppression materials. Moreover, the proposed approach is generalizable and can be applied to other cities around the world. 94 CHAPTER 5: QUANTITATIVE ASSESSMENT OF HUMAN ERROR FACTORS 5.1. Background According to the NFID, ~ 58.5% of fire incidents in SG-MURBs of the BC seven cities were caused by human errors [32]. Therefore, assessing human error factors that cause fire incidents (HEFs-FIs) is a significant fire risk management task in SG-MURBs. Evidential reasoning (ER) is a practical assessment method for dealing with uncertainties, including ignorance and randomness, resulting from limitations of qualitative and quantitative data for adequate decision support. The ER assessment process is based on defining weight for each attribute, the degree of belief that represents grades-based occurrence for all the grades of a specific attribute, and the unassigned degree of belief representing the extent of incompleteness in the assessment. ER could be used to assess fire risk causal factors, taking into consideration different distinctive evaluation grades with a degree of belief [438]–[441]. The causal factors of fire outbreaks, such as building material deterioration [228][442], igniting objects (e.g., frying pan, oven) [73][74], and occupant characteristics (e.g., temporary loss of judgment due to alcohol, sleep, fatigue as sub-attributes) [75] can be considered attributes in ER. Among various causal factors, human errors were the primary causes of fire incidents [258][259]. Considering the previous studies investigating human factors and building’s fire risks, as summarized in Table 3-6, limited HEFs-FIs have been considered in the existing studies. Furthermore, to the best of the authors’ knowledge, investigating HEFs-FIs quantitatively in SG-MURBs is missing. Therefore, this study intends to answer four important questions to enhance fire safety in SG-MURBs: (i) What are the possible HEFs that could cause fire incidents in SG-MURBs? (ii) What are the most critical HEFs-FIs in SG-MURBs? (iii) How to assess HEFs-FIs effectively at different stages, such as cities and provinces, to help decision-makers developing best fire safety practices? (iv) How to predict HEFs-FIs effectively and 95 efficiently? To fill the knowledge gap, an approach is developed to quantitatively assess HEFs that contribute to the outbreak of fire in SG-MURBs. 5.2. Methodology As shown in Figure 5-1, an approach was developed to identify, analyze, and evaluate HEFs-FIs in SG-MURBs. The approach identified important HEFs-FIs and analyzed their relative frequencies in SG-MURBs to establish probability distributions providing the optimum relative frequency range for each attribute. The relative frequency data could be collected from reliable historical records and fire incidents databases such as the National Fire Information Database (NFID). INCENDIARY FIRESMISUSE OF SOURCE OF IGNITIONMISUSE OF MATERIAL IGNITEDMECHANICAL/ELECTRICAL FAILURE/MALFUNCTIONMISUSE OF EQUIPMENTHUMAN FAILINGCategorizationIdentificationInvestigate all potential HEFs-FIs in SG-MURBsHEF 1HEF 2HEF 3HEF 4HEF 5HEF 6HEF 7HEF 8HEF 9HEF 10HEF n-1HEF nInvestigation of HEFs-FIs in SG-MURBs AnalysisRelative frequency and impactsFitting distributionPercentiles of the probability distributions EvaluationEvidential reasoningFitting distributionPercentiles of the probability distributions ANN modelPerformanceTraining stateError histogramRegressionSimulation ANN-based modellingClassificationPredictionConfusion matrixROC curveModels accuracy A benchmark was also developed to evaluate the relative frequencies of HEFs-FIs using different grades based on percentiles of the probability distributions. Then, the ER was used to aggregate HEFs-FIs for an effective assessment. Models were developed to predict human error factors and fire origin using various classifiers and Artificial Neural Network (ANN). Figure 5-1: Approach for assessing human error factors that induce fire incidents in SG-MURBs 96 5.2.1. Identification and analysis of human error factors that induce fire incidents This step identifies relevant HEFs-FIs in SG-MURBs. Based on the records of incidents, thirty-five HEFs-FIs were relevant to fire incidents in SG-MURBs, which are classified under six main attributes, as presented in Table 5-1. The first step is to determine relative frequencies (Fr) of HEFs in a specific city: 𝐹𝑟𝑖 =𝑁𝑖∑ 𝑁𝑖 𝑚𝑖=1⁄ ∀𝑖 = {1,2, … ,𝑚} 𝐸𝑞 (5 − 1) where 𝐹𝑟𝑖 is the relative frequency of the ith HEF; Ni presents the occurrence number of fire incidents caused by the ith HEF per year in one city; and ∑ 𝑁𝑖 𝑚𝑖=1 refer to the total number of fire incidents due to all identified HEFs-FIs in the same city. The Fr was calculated for each HEFi-FIs in the investigated cities. The suitable probabilistic distribution function (PDF) was identified for each HEF-FIs based on the frequencies resultant from seven cities using @RiskTM 7.6 (Palisade Corp., USA) [428]. Cumulative distribution functions can be developed based on the resulting PDFs. Selecting the most appropriate fitting distribution function was based on different criteria, including, first, PDF [433][434] providing a relative frequency of the HEFs-FIs in the set of possible values. Second, the relative frequencies mean of the HEF-FI. Third, the Skewness [416][434] of a PDF curve as a measure of the irregularity of the probability distribution of a HEF-FI about its mean. The skewness could be positive or negative if the tail is more or less than the mean, respectively. The skewness could be undefined if the PDF is symmetrical, normal distribution. HEF-FI distribution is highly skewed in case the value of skewness is less than (-1) or greater than (+1); HEF-FI is moderately skewed in case the skewness value is 97 between (-1) and (-½) or between (+½) and (+1); HEF-FI is approximately symmetric in case the value of skewness is between (-½) and (+½). HEFs-FIs Code INCENDIARY FIRES (Attribute 1) A1 Incendiary fire 11 Suspicious conditions 12 Riot, civil disturbance 13 Vandalism, mischief 14 MISUSE OF SOURCE OF IGNITION (Attribute 2) A2 Smokers' material 21 Thawing 22 Inadequate control of "open" fire 23 Torch too close 26 Misuse of source of ignition - unclassified 29 MISUSE OF MATERIAL IGNITED (Attribute 3) A3 Flammable liquid to kindle fire 33 Improper container 35 Overheated cooking oil, grease, wax 36 Combustible placed too close to heat 37 Improper storage 38 Misuse of material ignited - unclassified 39 MECHANICAL/ELECTRICAL FAILURE/MALFUNCTION (Attribute 4) A4 Part failure, leak, break 41 Electrical short circuit 44 Part worn out 46 Mechanical/electrical failure/malfunction - unclassified 49 MISUSE OF EQUIPMENT (Attribute 5) A5 Lack of maintenance 53 Equipment unattended 54 Misuse of equipment - unclassified 59 HUMAN FAILING (Attribute 6) A6 Asleep (excludes suspected use of alcohol, drugs or medication) 61 Temporary loss of judgement suspected (includes panic) 62 Physical or mental disability 63 Accident 64 Suspected impairment - use of alcohol, drugs or medication 65 Asleep - suspected use of alcohol, drugs or medication 66 Distracted, preoccupied 68 Human failing - unclassified 69 Fourth, Kurtosis [416][434] indicating the peakedness of a curve was used to define the distribution scale. A high Kurtosis value indicates that the HEF-FI has heavy tails (more observations in its tails). However, the modestly sized deviations indicating that the HEF-FI has light tails (fewer observations in its tails and more in the center). A kurtosis of a value 3 is considered to be a normal distribution. Kurtosis value-based names are given to distribution; Mesokurtic is given to distribution for kurtosis approximate to 3. Platykurtic is a distribution Table 5-1: Human error factors that cause fire incidents in SG-MURBs 98 with kurtosis less than 3, and Leptokurtic is a name for distribution with kurtosis more than 3. The most fitted distribution in this study is based on Akaike Information Criteria (AIC) that is chosen to rank fits; a lower AIC value indicates a better fit [426][427]. The probability distributions equations are presented in the appendix section for chapter 5 from Eq(A − 5 −1) to Eq(A − 5 − 21). 5.2.2. A benchmark and evidential reasoning-based HEFs-FIs assessment Relative frequencies-based benchmarks were developed to evaluate HEFs-FIs for safety improvement purposes taking into account the defined PDF for each HEF-FI. Then, selecting specific percentages based on expert judgment or literature review. In this study, 10%, 30%, 50%, 70%, and 90% were chosen to present five grades, including very low (VL), low (L), medium (M), high (H), and very high (VH) as evaluation grades for each HEF-FI. A 90% confidence level was chosen in this study as the maximum expected interval estimates of values our true value lies in; in case of data more than 90% occurs will fall automatically under the VH grade. However, the values of relative frequencies of different HEFs-FIs at the same grade may differ significantly. Now, each HEF-FI will have a relative frequency and an evaluated grade using the developed benchmark. The population for those cities was taken into consideration to investigate the relative frequency of HEFs-FIs per capita. Evidential reasoning (ER) was applied to assess and analyze decision-based systems that have both quantitative and qualitative criteria under various uncertainties, including randomness [441][445], following several steps. The attributes [𝐸𝑖, ∀𝑖 = (1,2, … , 𝑛)] associate to assess a general attribute (GA), which is the contribution of human error factors to a fire incident. Taking into account N distinctive evaluation grades to assess the defined basic attributes (ei) 99 (HEFs-FIs)[𝐻 = {𝐻1, 𝐻2, … , 𝐻𝑛, … 𝐻𝑁}], where Hn is the nth evaluation grade. The assessment of HEFs-FIs (𝐴𝑆(𝑒𝑖)) could be presented mathematically as follows: 𝐴𝑆(𝑒𝑖) = {(𝐻𝑛, 𝛽𝑛,𝑖), 𝑛 = (1,… , 𝑁)} 𝑖 = (1,… , 𝐿) 𝐸𝑞(5 − 2) where 𝛽𝑛,𝑖 (𝛽𝑛,𝑖 ≥ 0, ∑ 𝛽𝑛,𝑖𝑁𝑛=1 ≤ 1) denotes a degree of belief for each grade. A degree of belief that is also referred to as a mass refers to a belief function rather than a probability distribution. The assessment presents that basic attribute ei is assessed to grade Hn with the degree of belief of 𝛽𝑛,𝑖, 𝑛 = 1,… ,𝑁. An assessment AS(ei) is complete if ∑ 𝛽𝑛,𝑖𝑁𝑛=1 = 1 and incomplete if ∑ 𝛽𝑛,𝑖𝑁𝑛=1 < 1, or a special case if ∑ 𝛽𝑛,𝑖𝑁𝑛=1 = 0,where 𝛽𝑛,𝑖 = 0 for all 𝑛 =1, … , 𝑁 denoting a complete lack of information on ei. 5.2.2.1. Weight-based grades HEFs-FIs analysis HEFs-FIs refer to the basic attributes (ei) associated with E as the frame of all the basic attributes in ER [440][446][447], where (ei) varies from 1 to L {𝑒𝑖(i = 1,2, … , 𝐿) } for each attribute and E indicates the frame of all the basic attributes [𝐸 = {𝑒1, 𝑒2, … 𝑒𝑖 …𝑒𝐿}] with specific weight 𝑤𝑖 for each basic attribute (ei) [𝑤 = {𝑤1, 𝑤2 … 𝑤𝑖 … 𝑤𝐿}] with {0 ≤ 𝑤𝑖 ≤ 1}. Weights are based on the relative frequency of HEFs-FIs, which can be calculated through the following equation: 𝑤𝑖 =𝐹𝑟𝑖∑ 𝐹𝑟𝑖 𝐼𝑖=1⁄ ∀𝑖 = {1,2, … ,𝑚} 𝐸𝑞(5 − 3) Where 𝑤𝑖 refers to the specific weight for an attribute (ei). wi is normalized (∑𝑤𝑖 =1, 𝑠𝑢𝑚 𝑜𝑓 𝑒𝑎𝑐ℎ subset 𝑜𝑓 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠(𝐸𝐼(𝑖)) = 1). In this step, the relative frequencies (Fr) 100 of HEFs-FIs are used instead of the experts’ judgments in ER to define each attribute's weight in a specific city as presented in Equation (5-1). 5.2.2.2. A degree of belief (𝜷𝒏,𝒊)-based probability mass (𝒎𝒏,𝒊) By considering 𝑚𝑛,𝑖 to be a basic probability mass indicating the degree of the ith basic attribute ei , which helps assess GA for nth grade (Hn). 𝑚𝐻,𝑖 is considered to be the remaining probability mass unassigned (RPMU) to any individual grade as far as ei is concerned. The following equations are used to determine 𝑚𝑛,𝑖 and 𝑚𝐻,𝑖: 𝑚𝑛,𝑖 = 𝑤𝑖 ∗ 𝛽𝑛,𝑖_𝐺𝑖𝑗 𝑛 = (1,… ,𝑁) 𝐸𝑞(5 − 4) 𝛽_𝐺𝑖𝑗 =𝑁_𝐺𝑖𝑗𝑛, ∀ 𝑖 = (1,2, … ,𝑚), ∀ 𝑗 = {𝑉𝐿, 𝐿,𝑀,𝐻, 𝑉𝐻} 𝐸𝑞(5 − 5) Where 𝛽_𝐺𝑖𝑗 is the degree of belief of grade (j) for an attribute (i), 𝑁_𝐺𝑖𝑗 is the number of a grade (j) for an attribute (i) within the considered cities, n is the total number of the cities under consideration, and 𝑚 is the total number of the attributes. 𝑚𝐻,𝑖 = 1 − ∑ 𝑚𝑛,𝑖 = 1 − 𝑤𝑖 ∑ 𝛽_𝐺𝑖𝑗𝑁𝑛=1𝑁𝑛=1 𝐸𝑞(5 − 6) 𝑚𝐻,𝑖 could be decomposed into two parts: (i) ?̅?𝐻,𝑖 and (ii) ?̃?𝐻,𝑖, where 𝑚𝐻,𝑖 = ?̅?𝐻,𝑖 + ?̃?𝐻,𝑖 ?̅?𝐻,𝑖 = 1 − 𝑤𝑖 𝑎𝑛𝑑 ?̃?𝐻,𝑖 = 𝑤_𝐺𝑖𝑗 (1 − ∑ 𝛽_𝐺𝑖𝑗𝑁𝑛=1) 𝐸𝑞(5 − 7) where ?̅?𝐻,𝑖 and ?̃?𝐻,𝑖 represent the first and the second part of the incompleteness in the assessment 𝐴𝑆(𝑒𝑖) of the RPMU for specific grades of attribute (ei). ?̃?𝐻,𝑖 will be zero if 𝐴𝑆(𝑒𝑖) is complete, or ∑ 𝛽_𝐺𝑖𝑗𝑁𝑛=1 = 1; otherwise, ?̃?𝐻,𝑖 will be positive. 101 5.2.2.3. Probability mass-based ER aggregation process The ER aggregation process uses different equations to aggregate probability masses of the attributes. The following ER algorithm combines the first i assessment with the (i+1)th assessment [446]: 𝑚𝑛,𝐼(𝑖+1) = 𝐾 𝐼(𝑖+1)[𝑚𝑛,𝐼(𝑖)𝑚𝑛,𝑖+1 + 𝑚𝐻,𝐼(𝑖)𝑚𝑛,𝑖+1 + 𝑚𝑛,𝐼(𝑖)𝑚𝐻,𝑖+1], 𝑛= (1,2, … ,𝑁 ) 𝐸𝑞(5 − 8) 𝑚𝐻,𝐼(𝑖) = ?̃?𝐻,𝐼(𝑖) + ?̅?𝐻,𝐼(𝑖) 𝐸𝑞(5 − 9) 𝐾 𝐼(𝑖+1) =[ 1 − ∑∑𝑚𝑡,𝐼(𝑖)𝑚𝑗,𝑖+1𝑁𝑗=1𝑗≠𝑡𝑁𝑡=1] −1 𝑖 = (1,2, … , 𝐿 − 1) 𝐸𝑞(5 − 10) ?̃?𝐻,𝐼(𝑖+1) = 𝐾 𝐼(𝑖+1)[?̃?𝐻,𝐼(𝑖)?̃?𝐻,𝑖+1 + ?̅?𝐻,𝐼(𝑖)?̃?𝐻,𝑖+1 + ?̃?𝐻,𝐼(𝑖)?̅?𝐻,𝑖+1] 𝐸𝑞(5 − 11) ?̅?𝐻,𝐼(𝑖+1) = 𝐾 𝐼(𝑖+1)[?̅?𝐻,𝐼(𝑖)?̅?𝐻,𝑖+1] 𝐸𝑞(5 − 12) Where 𝑚𝑛,𝑖 is the basic probability mass indicating the degree of the ith basic attribute ei. 𝑚𝐻,𝐼(𝑖) is the RPMU to any individual grades. 𝐾𝐼(𝑖+1) is a normalizing factor, where ∑ 𝑚𝑛,𝐼(𝑖+1)𝑁𝑛=1 + 𝑚𝐻,𝐼(𝑖) = 1. 𝑚𝑡,𝐼(𝑖) is referring the probability mass of attribute i at evaluation grade (t). 𝑚𝑛,𝐼(𝑖), 𝑚𝐻,𝐼(𝑖), ?̃?𝐻,𝐼(𝑖), ?̅?𝐻,𝐼(𝑖) are the combined probability masses created through aggregating the first i assessments. 𝛽𝑛 =𝑚𝑛,𝐼(𝐿)1 − ?̅?𝐻,𝐼(𝐿) 𝑛 = 1,2, … ,𝑁 𝐸𝑞(5 − 13) 𝛽𝐻 =?̃?𝐻,𝐼(𝐿)1 − ?̅?𝐻,𝐼(𝐿) 𝐸𝑞(5 − 14) 102 𝐴𝑆(𝐺𝐴) = {(𝐻𝑛, 𝛽𝑛), 𝑛 = {1,… ,𝑁} 𝐸𝑞(5 − 15) where 𝛽𝑛 is the degree of belief to which 𝐻𝑛 is assessed. 𝛽𝐻 is the unassigned degree of belief signifying the extent of incompleteness in the overall human error assessment. The ER could be implemented using Intelligence Decision System to generate the simulation with precise result assessment. 5.2.3. Prediction analysis: classification and artificial neural network-based regression 5.2.3.1. Correlation and association analysis In this study, five main predictors were investigated, including elder people (EP), young people (YP), lone-parent (LP), and factors of ignition sources. Besides, attributes and factors of human error factors (HEFs-FIs) and fire origin (FO) were considered responses. The related HEFs-FIs and origin of fire attributes in SG-MURBs were investigated based on the occurred fire incidents. Whereas elder people refers to the percentage of the population aged 65 years and older; young people refer to the percentage of the population aged five years and younger; lone-parent indicates the percentage of family dwellings in the selected geography that are single-parent families. Five levels of the predictors, elder people, young people, and lone-parent are determined based on the recorded percentages in NFID. The levels include very high (VH=5), high (H=4), medium (M=3), low (L=2), and very low (VL=1). The levels were determined based on a moving average, analyzing fire impacts of the full data set considering a series of averages of different subsets, as follows [(max) | average (max, average) | average | average (average, min)| average (average (average, min), min)], respectively (Table 5.2). Correlation and association analysis was performed among the predictors and the responses. Variables were selected considering p-value and multicollinear; in this study, variables with p-value =< 0.05 and a spearman correlation coefficient > 0.5 were selected. 103 Table 5-2: The determined levels of the predictors for the seven cities Levels Elder people (EP, %) Young people (YP, %) Lone-parent (LP,%) VL (1) 6.40 3.30 21.00 L (2) 9.81 4.91 22.49 M (3) 13.23 6.53 23.98 H (4) 18.61 8.86 26.29 VH (5) 24.00 11.20 28.60 5.2.3.2. Classification analysis Various classifiers, such as decision trees, discriminant analysis, support vector machines, nearest neighbors, and ensemble classification, were used in this study to help explore fire incident data, select potential factors, train models, and assess fire impacts using MATLAB® R2018b. The classifiers help to predict human error factors that induce fire incidents considering several predictors, such as elder people (EP), young people (YP), and lone-parent (LP) in SG-MURBs. 5.2.3.3. Artificial neural network ANN model was developed to predict fire origin (FO), an attribute A(FO), HEFs-FIs, and an attribute of A(HEFs-FIs) (Table 5-1 and Table 5-3) in SG-MURBs considering the predictors, ISF-FIs, elder people (EP), young people (YP), and lone-parent (LP). Three algorithms were used to develop the ANN model, including Lavenberg-Marquardt (LM) [431], Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) backpropagation algorithms. The percentages of the data used for training, validation, and testing of each model are 70%, 15%, and 15%, respectively. The performance of the model is determined using mean squared error (MSE), correlation coefficient (R), and coefficient of determination (R2). The advanced script was generated to produce results and solve similar problems reusing the sequences of commands. Furthermore, Simulink models were generated to simulate ANN efficiently considering different components, such as a sine wave component, a dashboard scope, and half gauge components. 104 Table 5-3: Attributes and factors of fire origin in SG-MURBs Attributes and factors of fire origin Code MEANS OF EGRESS (Attribute) 01 Hallway, corridor 10 Stairway, exterior (includes fire escape, ramp) 20 Stairway, interior (includes ramp) 30 Lobby, entranceway 50 Means of egress - unclassified 90 ASSEMBLY, FAMILY, SALES AREA (GROUPS OF PEOPLE) 1 Small assembly area - 60 people or less (includes classroom, meeting room with or without fixed seats) 130 Lounge, living room (includes common room, TV room, family room, sitting room) 140 Assembly, sales area – unclassified 190 FUNCTION AREA 2 Sleeping area (includes bedroom, lockup) 210 Dining area (includes mess room, canteen, lunchroom, cafeteria) 230 Washroom, locker area 250 Office 260 Kitchen, cooking area 310 Laundry area (includes wash house) 320 Electronic equipment room (includes electronic computer area, data processing centre) 350 Function area - unclassified 390 STORAGE AREA - INSIDE BUILDING 4 Closet (includes lockers) 420 Supplies, equipment area (includes tool room, maintenance supply room, supply room) 430 Trash, rubbish area 460 Vehicle storage (includes garage, carport) 470 Storage area - unclassified 490 SERVICE FACILITIES 5 Service shaft (includes pipe, conduit, vent) 520 Duct - heating, air conditioning 550 Duct - cooking 560 Chimney, flue pipe, gas vent 580 Service facilities - unclassified 590 SERVICE AND EQUIPMENT AREA 6 Machinery room (includes elevator machinery room, engine room, pump room, refrigeration room) 610 Heating equipment room (includes furnace room, water heater closet, boiler room) 620 Transformer vault (includes ancillary switchgear) 630 Incinerator room (includes all incinerator operations, chute) 640 Service and equipment area - unclassified 690 STRUCTURAL AREA 7 Crawl space (includes sub-structure space) 710 Exterior balcony (includes open porch) 720 Ceiling and floor assembly (includes concealed floor/ceiling space) 730 Ceiling and roof/ceiling space 740 Wall assembly (includes concealed wall space) 750 Exterior wall 760 Exterior roof 770 Awning, canopy 780 Structural area - unclassified 790 OUTSIDE AREA 9 Open area (includes lawn, field, farmyard, park, pier, wharf) 910 Court, patio, terrace 920 Parking area 930 Storage area (includes outdoor shipping facilities other than loading platforms attached to buildings) 940 Trash, rubbish area 950 Outside area – unclassified 990 105 5.3. Results and Discussions 5.3.1. Analysis of human error factors that induce fire incidents in SG-MURBs Twenty-eight HEFs-FIs under six attributes were identified to be responsible for fire incidents in SG-MURBs, as shown in Table 5-4. The relative frequencies of the HEFs-FIs for the seven cities are also shown in the table. The table shows that equipment unattended is the most critical attribute in Surrey with a relative frequency of 1.87E-01 per year, followed by the incendiary fire of 8.68E-02. The critical attributes in Victoria included equipment unattended, incendiary fire, and vandalism/mischief with relative frequencies of 1.62E-01, 1.35E-01, and 8.11E-02, correspondingly. Equipment unattended (3.23E-01), and vandalism/mischief (9.68E-02) are among the most critical attributes in Vernon city. The most critical HEFs-FIs in Kelowna was an incendiary fire with a relative frequency of 1.73E-01. In Kamloops, incendiary fire and smokers' material were the top two critical HEFs-FIs with relative frequencies of 5.35E-02 and 3.74E-02, respectively. Equipment unattended (1.49E-01) and vandalism/mischief (1.05E-01) were the two top HEFs-FIs in Vancouver. Lastly, vandalism/mischief was also the most critical HEF-FIs in West-Kelowna, with a relative frequency of 1.32E-01. It can be seen that the relative frequencies of the HEFs-FIs vary significantly from one city to another. Table 5-5 presents the information of fitted distribution, such as alpha (α) and beta (β) as error type I, and II, respectively, for the 28 HEFs-FIs. It can be seen that the HEFs-FIs fall under five different types of distributions; 42.86% of HEFs-FIs were found to be following Gamma distribution. The rest of the HEFs-FIs were following Triangular, Exponential, Weibull, and Pearson5 distributions with 28.57%, 17.14%, 8.57%, and 2.86%, respectively. 106 N HEFs Frequency/year Surrey Victoria Vernon Kelowna Kamloops Vancouver West-Kelowna A1 INCENDIARY FIRES 1 Incendiary fire 8.68E-02 1.35E-01 6.45E-02 1.73E-01 5.35E-02 9.26E-02 5.26E-02 2 Suspicious 0.00E+00 0.00E+00 0.00E+00 3.70E-02 2.41E-02 0.00E+00 0.00E+00 3 Riot, civil disturbance 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.52E-03 0.00E+00 4 Vandalism, mischief 6.32E-02 8.11E-02 9.68E-02 0.00E+00 0.00E+00 1.05E-01 1.32E-01 A2 MISUSE OF SOURCE OF IGNITION 5 Smokers' material 0.00E+00 0.00E+00 0.00E+00 4.94E-02 3.74E-02 0.00E+00 0.00E+00 6 Thawing 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.67E-03 0.00E+00 0.00E+00 7 Inadequate control of "open" fire 5.26E-03 0.00E+00 0.00E+00 0.00E+00 5.35E-03 1.82E-02 2.63E-02 8 Torch too close 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.67E-03 0.00E+00 0.00E+00 A3 MISUSE OF MATERIAL IGNITED 9 Flammable liquid to kindle fire 0.00E+00 0.00E+00 0.00E+00 1.23E-02 0.00E+00 0.00E+00 0.00E+00 10 Improper container 0.00E+00 0.00E+00 0.00E+00 3.70E-02 2.67E-03 0.00E+00 0.00E+00 11 Overheated cooking oil, grease, wax 0.00E+00 0.00E+00 0.00E+00 7.41E-02 1.87E-02 0.00E+00 0.00E+00 12 Combustible placed too close to heat 0.00E+00 0.00E+00 0.00E+00 2.47E-02 0.00E+00 0.00E+00 0.00E+00 13 Improper storage 5.26E-03 2.70E-02 0.00E+00 0.00E+00 0.00E+00 6.07E-03 0.00E+00 A4 MECHANICAL/ELECTRICAL FAILURE/MALFUNCTION 14 Part failure, leak, break 0.00E+00 0.00E+00 0.00E+00 1.00E-06 8.02E-03 0.00E+00 0.00E+00 15 Electrical short circuit 0.00E+00 0.00E+00 0.00E+00 1.23E-02 8.02E-03 0.00E+00 0.00E+00 16 Part worn out 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.67E-03 0.00E+00 0.00E+00 17 Mechanical/electrical failure/malfunction - unclassified 7.89E-02 5.41E-02 9.68E-02 8.64E-02 2.41E-02 9.26E-02 2.63E-02 18 Design deficiency 2.63E-03 0.00E+00 0.00E+00 0.00E+00 2.67E-03 7.59E-03 0.00E+00 19 Construction deficiency 0.00E+00 0.00E+00 3.23E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 A5 MISUSE OF EQUIPMENT 20 Lack of maintenance 1.05E-02 0.00E+00 0.00E+00 0.00E+00 5.35E-03 2.28E-02 0.00E+00 21 Equipment unattended 1.87E-01 1.62E-01 3.23E-01 0.00E+00 0.00E+00 1.49E-01 7.89E-02 A6 HUMAN FAILING 22 Asleep (excludes suspected use of alcohol, drugs or medication) 0.00E+00 0.00E+00 0.00E+00 0.00E+00 8.02E-03 0.00E+00 0.00E+00 23 Temporary loss of judgement suspected (includes panic) 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.35E-03 0.00E+00 0.00E+00 24 Physical or mental disability 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.67E-03 0.00E+00 0.00E+00 25 Accident 0.00E+00 0.00E+00 0.00E+00 1.23E-02 2.67E-03 0.00E+00 0.00E+00 26 Suspected impairment - use of alcohol, drugs or medication 0.00E+00 0.00E+00 0.00E+00 1.23E-02 1.34E-02 0.00E+00 0.00E+00 27 Asleep - suspected use of alcohol, drugs or medication 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.35E-03 0.00E+00 0.00E+00 28 Distracted, preoccupied 0.00E+00 0.00E+00 0.00E+00 1.23E-02 8.02E-03 0.00E+00 0.00E+00 Note: A1-A6: attributes from 1 to 6 Table 5-4: Relative frequencies of HEFs-FIs in seven cities 107 Table 5-5 presents the mean value of each fitted distribution of HEF-FI followed with standard deviation (Std Dev) to show how the values for a HEF-FI group were spread out from the mean. Where the Std Dev values of HEFs-FIs were very low, which means most of the numbers were very close to the mean value, and the distributions were well fitted to the recorded relative frequencies of HEFs-FIs. The table also shows that thirty-three HEFs-FIs were positively skewed, whereas just two HEFs-FIs were negatively skewed. Table 5-5: Best fit probability distributions for HEFs-FIs HEFs-FIs Fitted distribution (α,β) Mean Std Dev 10% (VL) 30% (L) 50% (M) 70% (H) 90% (VH) 1 Pearson5 (6.1799,0.48768) 0.09415 0.04605 0.05132 0.06769 0.08336 0.10427 0.1485 2 Expon(0.0087287) 0.008729 0.008729 0.00092 0.003113 0.00605 0.010509 0.020099 3 Expon(0.00021678) 0.0002167 0.0002167 2.28E-05 7.73E-05 0.0001502 0.000261 0.00049915 4 Gamma(16.912,0.0056446) 0.09546 0.02321 0.06718 0.08207 0.09359 0.10614 0.12616 5 Expon(0.012402) 0.012402 0.012402 0.001307 0.004423 0.008596 0.014932 0.028557 6 Expon(0.00038197) 0.000382 0.000382 4.02E-05 0.0001362 0.0002648 0.0004599 0.0008795 7 Gamma(2.1835,0.0063129) 0.013784 0.009328 0.00397 0.00785 0.011747 0.016781 0.026264 8 Expon(0.00038197) 0.000382 0.000382 4.02E-05 0.0001362 0.0002648 0.0004599 0.0008795 9 Expon(0.0017637) 0.0017637 0.0017637 0.0001858 0.0006291 0.0012225 0.0021234 0.0040611 10 Triang(0,0,0.042379) 0.014126 0.009989 0.002175 0.006922 0.012413 0.019167 0.028978 11 Gamma(2.4268,0.019118) 0.0464 0.02978 0.0146 0.02753 0.0402 0.05632 0.08628 12 Triang(0,0,0.028219) 0.009406 0.006651 0.001448 0.004609 0.008265 0.012763 0.019295 13 Triang(0,0,0.03115) 0.010383 0.007342 0.001599 0.005088 0.009124 0.014088 0.0213 14 Triang(0,0,0.0091673) 0.0030558 0.0021608 0.0004704 0.0014974 0.002685 0.0041462 0.0062683 15 Gamma(21.845,0.00046617) 0.010183 0.002179 0.00751 0.008938 0.010029 0.011204 0.013056 16 Triang(0,0,0.0030558) 0.0010186 0.0007203 0.0001568 0.0004991 0.000895 0.0013821 0.0020895 17 Weibull(2.571,0.074059) 0.06576 0.02744 0.03086 0.04959 0.06422 0.0796 0.10244 18 Gamma(3.9413,0.0010904) 0.004298 0.002165 0.00186 0.002958 0.00394 0.00512 0.007199 19 Triang(0,0,0.036866) 0.012289 0.008689 0.001892 0.006022 0.010798 0.016674 0.025208 20 Gamma(3.0916,0.0041657) 0.012879 0.007325 0.004826 0.008292 0.01152 0.015503 0.0227 21 Gamma(5.2157,0.034482) 0.17985 0.07875 0.08915 0.13183 0.16849 0.21144 0.28524 22 Triang(0,0,0.0091673) 0.0030558 0.0021608 0.0004704 0.0014974 0.002685 0.0041462 0.0062683 23 Triang(0,0,0.0061115) 0.0020372 0.0014405 0.0003136 0.0009983 0.00179 0.0027641 0.0041789 24 Triang(0,0,0.0030558) 0.0010186 0.0007203 0.0001568 0.0004991 0.000895 0.0013821 0.0020895 25 Gamma(2.0179,0.0037216) 0.00751 0.005287 0.002014 0.004137 0.006312 0.009158 0.014575 26 Gamma(631.13,0.0000203718) 0.012857 0.000512 0.012206 0.012584 0.01285 0.013121 0.013517 27 Triang(0,0,0.0061115) 0.0020372 0.0014405 0.0003136 0.0009983 0.00179 0.0027641 0.0041789 28 Gamma(21.845,0.00046617) 0.010183 0.002179 0.00751 0.008938 0.010029 0.011204 0.013056 The HEFs-FIs were not perfectly symmetrical as skewness does not equal 0; however, 20.00% of the HEFs-FIs distribution was approximately symmetric. About 45.71% of the HEFs-FIs distributions were highly skewed, and 34.29% were moderately skewed. Based on the Kurtosis value, taking into account an approximation of ±0.5 to the value 3, 20.00% of the distributions were following Mesokurtic. Where 28.57% of the distributions were found to be following Platykurtic, and the highest percentage, 51.43%, of HEFs-FI distributions were found to be 108 following Leptokurtic as the distribution. According to the mean values of the fitted distributions, it can be seen that the highest relative frequency of HEFs-FIs was unattended equipment with a relative frequency of 1.8E-01, followed by misuse of equipment with a relative frequency of 1.5E-01. Lastly, the table presents five percentile values for each HEF-FIs at 10%, 30%, 50%, 70%, and 90%, corresponding to the grades VL, L, M, H, and VH. 5.3.2. Benchmarking of HEFs-FIs Figure 5-2 presents the developed benchmark for some of the identified HEFs-FIs based on the given percentages of their fitted distributions in Table 5-5. The benchmark helped to determine the relative frequency-based grades for each HEF-FI. Also, it helped to compare HEFs-FIs for different cities for safety improvement purposes. Based on the HEFs-FIs benchmark, Table 5-6 presents the grades of all the HEFs-FIs for the seven cities showing the results of criticality analysis based on the high relative frequency. Figure 5-3 clusters the HEFs-FIs, presented in Table 5-6, for each city based on their grades. The first two highest grades were taken into consideration for pessimistic analysis: very high Figure 5-2: Benchmarking for the first 19 human error factors that cause fire incidents 109 (VH) and high (H) to define cities with critical HEFs-FIs. It can be seen that Kelowna had the highest percentage of HEFs with 37.14% VH relative frequency-based grade (VH-HEFs) followed by Kamloops with 34.29% of VH-HEFs. Vancouver, Victoria, West-Kelowna, and Surrey were associated with 14.29%, 11.43%, 11.43%, and 5.71% of VH-HEFs, respectively. For HEFs-FIs with high grade (H-HEFs), Surrey is the city with the highest percentage of H-HEFs (14.29%), followed by Vancouver (11.43%), Vernon (8.57%), Victoria and West-Kelowna (8.57%), and Kelowna (0%). These results help the cities to develop a specific safety strategic plan to control the critical HEFs-FIs. Also, at the provincial level, the results help the policy-makers to emphasize the critical aspects. Table 5-6: Relative frequency-based grades and criticality analysis of all the HEFs-FI at the seven cities HEFs-FIs Surrey Victoria Vernon Kelowna Kamloops Vancouver West-Kelowna Surrey Victoria Vernon Kelowna Kamloops Vancouver West-Kelowna 1 H VH L VH L H L 35 26 25 35 35 35 35 2 VL VL VL VH VH VL VL 25 35 26 1 15 26 9 3 VL VL VL VL VL VH VL 26 25 35 34 1 25 26 4 VL L H VL VL H VH 1 1 4 19 5 4 4 5 VL VL VL VH VH VL VL 19 4 19 12 2 1 25 6 VL VL VL VL VH VL VL 15 19 1 15 19 19 15 7 L VL VL VL L VH VH 4 14 15 5 12 15 1 8 VL VL VL VL VH VL VL 9 15 9 2 31 9 7 9 VL VL VL VH VL VL VL 24 2 22 9 16 7 2 10 VL VL VL VH L VL VL 7 3 2 13 17 23 3 11 VL VL VL VH L VL VL 14 5 3 10 27 22 5 12 VL VL VL VH VL VL VL 22 6 5 17 33 20 6 13 M VH VL VL VL M VL 20 7 6 23 7 14 8 14 VL VL VL VL VH VL VL 3 9 8 31 24 2 11 15 VL VL VL VH L VL VL 5 10 10 33 28 5 12 16 VL VL VL VL VH VL VL 6 11 11 16 32 6 13 17 H M VH VH VL VH VL 8 12 12 3 6 8 14 18 L VL VL VL L VH VL 10 13 13 4 8 10 16 19 VL VL VH VL VL VL VL 11 16 14 6 11 11 17 20 M VL VL VL L VH VL 16 20 18 14 22 16 21 21 H M VH VL VL M VL 17 21 20 18 26 17 22 22 VL VL VL VL VH VL VL 21 24 24 21 30 21 24 23 VL VL VL VL VH VL VL 27 27 27 22 3 27 27 24 VL VL VL VL VH VL VL 28 28 28 24 4 28 28 25 VL VL VL VH L VL VL 29 29 29 25 10 29 29 26 VL VL VL L VH VL VL 30 30 30 26 13 30 30 27 VL VL VL VL VH VL VL 31 31 31 27 14 31 31 28 VL VL VL VH L VL VL 32 32 32 28 21 32 32 Grades of the attributes in the seven cities Criticality analysis of HEFs-FI 110 The population of the investigated cities was taken into account in this study to examine the population's influence on the relative frequencies-based grades of HEFs-FIs. Statistics of the seven cities' population were taken from Statistics Canada, 2016 Census of Population [448]. Figure 5-3 shows comparisons among the cities in terms of the grades of HEFs-FIs with and without considering the population for five different grades. Focusing on VH-HEFs per capita, Kelowna was found to be also having the highest percentage of VH-HEFs per capita (49%). It was followed by Kamloops with 23% of VH-HEFs per capita. Vancouver came in third place with 20% of VH-HEFs per capita, followed by Vernon (14%) and Victoria (3%). By comparing the results with and without the integration of population, the cities' classification based on the VH grade shows a slight difference was recorded in terms of classifying the most critical cities. The order of the cities based on VH-HEFs percentages is Kelowna, Kamloops, Vancouver, Victoria, Vernon, West-Kelowna, and Surrey. Therefore, the population does not have an influence on the classification criticality of HEFs-FIs in the cities. 0%20%40%60%80%100%Fr Fr-Pop Fr Fr-Pop Fr Fr-Pop Fr Fr-Pop Fr Fr-Pop Fr Fr-Pop Fr Fr-PopSurrey Victoria Vernon Kelowna Kamloops Vancouver West-KelownaVL L M H VHFigure 5-3: Comparison between grades of HEFs that cause fire incidents 111 This analysis helps to define the critical attributes and basic attributes (factors) that deserve more attention in fire risk management for each city. The most common and critical factors that all the cities should be taking into account for prevention and protection are determined. The common and critical factors are identified, including incendiary fire, misuse of material ignited, mechanical/electrical failure/malfunction, equipment unattended, misuse of source of ignition, misuse of equipment, vandalism, mischief, suspicious, smokers material, inadequate control of open fire, overheated cooking oil, grease, wax, construction, design or installation deficiency, and lack of maintenance. 5.3.3. Application of evidential reasoning The evidential reasoning approach was used to aggregate the HEFs-FIs for the overall assessment. The ER assessment of HEFs-FIs in SG-MURBs at the BC province level is based on the qualitative and quantitative assessment of HEFs-FIs at cities level with the following evaluation grades: HEF-FI = {VL, L, M, H, VH}. HEFs-FIs were the basic attributes and categorized under six main attributes, as shown in Table 5-7. The table shows evaluation grades for all the 28 basic attributes with the normalized weight for each attribute (∑𝑤𝑖 = 1). Then, ?̅?𝐻,𝑖 and ?̃?𝐻,𝑖 as the first and the second part of the RPMU to the basic attributes are determined. Equations (5-1 to 5-12) were applied to generate the assessment percentages for the attributes and their basic attributes. Intelligence Decision System for Multiple Criteria Assessment was used to generate the simulation with precise result assessment, as shown in Figure 5-4, taking into account the results of the analyzed HEFs-FIs in Table 5-7. The figure presents percentages stating the comparison results of evidential reasoning with the average-based conventional method; evidential reasoning tends to have high percentages for 112 pessimistic grades achieving its main objective. The figure shows a list of typical attributes in a subjective judgment presenting degrees of belief and evaluation grades. Note: 1: incendiary fires; 2: misuse of source of ignition; 3: misuse of material ignited, 4: mechanical/electrical failure/malfunction; 5: misuse of equipment; 6: human failing; CM: conventional method; ER: evidential reasoning Figure 5-4: Assessment of HEFs-FIs that cause fire incidents in BC’s SG-MURBs 65.7%7.9%4.9%4.0%17.5%59.6%7.2%5.5%7.1%20.7%VLLMHVHEvidential reasoning Conventional methodVH H 113 Degree of belief (β) HEFs-FIs (sub-attributes) Evaluation grades Normalized weight RPMU ?̅?𝑯,𝒊 ?̃?𝑯,𝒊 VL L M H VH General attribute (HEF-FI) 6 Main Attributes 1 0.000 0.429 0.000 0.286 0.286 0.4739 0.5260 5.261E-17 2 0.714 0.000 0.000 0.000 0.286 0.0439 0.9560 0 3 0.857 0.000 0.000 0.000 0.143 0.0010 0.9989 0 4 0.429 0.143 0.000 0.286 0.143 0.4809 0.5190 0 5 0.714 0.000 0.000 0.000 0.286 0.1512 0.8487 0 6 0.857 0.000 0.000 0.000 0.143 0.0046 0.9953 0 7 0.429 0.286 0.000 0.000 0.286 0.1683 0.8316 1.869E-17 8 0.857 0.000 0.000 0.000 0.143 0.0046 0.9953 0 9 0.857 0.000 0.000 0.000 0.143 0.0121 0.9878 0 10 0.714 0.143 0.000 0.000 0.143 0.0973 0.9026 0 11 0.714 0.143 0.000 0.000 0.143 0.3205 0.6794 0 12 0.857 0.000 0.000 0.000 0.143 0.0649 0.9350 0 13 0.571 0.000 0.286 0.000 0.143 0.0718 0.9281 0 14 0.857 0.000 0.000 0.000 0.143 0.0162 0.9837 0 15 0.714 0.143 0.000 0.000 0.143 0.0542 0.9457 0 16 0.857 0.000 0.000 0.000 0.143 0.0054 0.9945 0 17 0.286 0.000 0.143 0.143 0.429 0.3496 0.6503 0 18 0.571 0.286 0.000 0.000 0.143 0.0228 0.9771 0 19 0.857 0.000 0.000 0.000 0.143 0.0653 0.9346 0 20 0.571 0.143 0.143 0.000 0.143 0.0372 0.9627 8.280E-18 21 0.429 0.000 0.286 0.143 0.143 0.5203 0.4796 1.155E-16 22 0.857 0.000 0.000 0.000 0.143 0.0307 0.9692 0 23 0.857 0.000 0.000 0.000 0.143 0.0204 0.9795 0 24 0.857 0.000 0.000 0.000 0.143 0.0102 0.9897 0 25 0.714 0.143 0.000 0.000 0.143 0.0753 0.9246 0 26 0.714 0.143 0.000 0.000 0.143 0.1294 0.8705 0 27 0.857 0.000 0.000 0.000 0.143 0.0204 0.9795 0 28 0.714 0.143 0.000 0.000 0.143 0.1023 0.8976 0 Table 5-7: Degrees of belief, importance weights, and the remaining probability mass unassigned (RPMU) to individual grades of the HEFs-FIs in BC 114 The results indicated that more than 20% of the defined HEFs-FIs in the seven cities were VH, where the actions are required immediately from the BC government for prevention and protection. About 7.07% of HEFs-FIs were H; therefore, actions and administrative practices should be given high priority. Roughly 5.46% of the events were under M grade, where safeguard installation and administrative practices should be addressed at the earliest convenience. Approximately 7.18% of the HEFs-FIs were under the L grade, where the safeguards installation and administrative practices can be reviewed or updated. 5.3.4. HEFs-FIs-based Prediction results 5.3.4.1. Correlation analysis The correlation analysis showed that the elder people (EP), young people (YP), and lone-parent (LP) predictors were found to be correlated with the response of HEFs-FIs (Table 5-1), as presented in Figure 5-5. The elder people, young people, and lone-parent predictors have R values of -0.825, 0.87, and -0.124, respectively, using the determined levels of the predictors, as presented in Table 5-2. Figure 5-5 shows that 68% of the total fire incidents were related to the high level of elder people; 64% of the fire incidents were correlated with a medium level of young people. Concerning lone parent, whereas 48.5% of the incidents were associated with the medium level. The analysis of fire incidents found that more than 80% of the fire incidents were associated with medium and high levels of the predictors, interchangeably. Furthermore, statistical test results found that p-value < 0.005; therefore, the association among the variables was statistically significant; besides, the variables were found to be correlated. Investigation of 3105 fire incidents based on the origin of fires in SG-MURBs revealed, as presented in Figure 5-6, that fire has occurred at specific places with different occurrence levels. The functioning area has been found to have the highest fire incidents, with 48% of the 115 recorded fire incidents; whereas 65% of these fires occurred in kitchens. It was followed by the outside area (13%) and structural area (12%), including the exterior balcony and open porch. Assembly of a family (groups of people), including lounge and living room, was found to have 8% of fire incidents, whereas means of egress were found to have just 6% of the occurred fire incidents. The major locations of fire origin in buildings help identify proper locations to install extinguishing systems for effective and efficient fire suppression. Note: EP: elder people, YP: young people, LP: lone-parent, and HEFs-FIs: human error factors that induce fire incidents. Furthermore, Figure 5-6 shows that the origin of the fire was associated with the predictors' levels; the low the level of elder people and lone parent, the higher was the probability of fire in the function area, whereas it was the opposite with young people. This confirms that keeping the elder alone and having a lone parent at home increases the chance of fires in the functional -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1EP/HEFs-FIsYP/HEFs-FIsLP/HEFs-FIsCorrelation Figure 5-5: Pareto charts of three predictors and their association with HEFs-FIs 116 areas as the elderly people might have difficulty keeping control of cooking equipment or alone parent are getting involved with other duties for kids' care, respectively. Consequently, the knowledge on the predictors helps predict the type of human errors and the origin of the fire in SG-MURBs. Predicting the potential human error factors that cause fire incidents helps to enhance fire prevention strategies accordingly. Note: EP: elder people, YP: young people, and LP: lone-parent. 0 200 400 600 800 1000 1200 1400 1600Multiple areas of originMeans of egressAssembly of family (groups of people)Function areaStorage area – inside building Service facilitiesService & equipment areaStructural areaVehicle areaOutside areaNumber of fire incidentsArea of fire origin attribute A(FO)0 200 400 600 800 1000 1200Sleeping areaDining areaWashroom and locker areaKitchenLaundry areaFunction area - unclassifiedNumber of fire incidentsArea of fire origin in function area0501001502002503001/11/21/71/81/92/12/22/42/52/62/72/82/93/13/23/43/53/63/73/94/14/24/44/74/84/95/25/45/75/9Fire incidentsEP/A(FO)01002003004005006002/23/13/23/43/53/73/94/14/24/44/54/64/74/84/95/15/25/45/65/75/85/9Fire incidentsYP/A(FO)01002003004005006007001/11/21/41/51/61/71/81/9LP/A(FO) Figure 5-6: Fire incidents in different building areas with LP, YP, and LP predictors 117 5.3.4.2. Classification results Performance results of HEFs-based classification using different algorithms, as presented in Figure 5-7, found that medium tree and ensemble boosted trees are the best classifier algorithms with an accuracy of 64%. They are followed by the fine tree and coarse tree algorithms with 62% and 61%, respectively. In contrast, the classifier algorithms with the lowest accuracy are cubic SVM and fine KNN with 39% and 52%, respectively. The confusion matrix shows that the medium tree has a performance ability of 72%, presented in the intersection cell of 1st column and 1st row, to classify incendiary fires correctly. The other factors in the first row are misclassified: 10% of the factors are incorrectly classified as misuse of the source of ignition, 3% are classified as mechanical/electrical failure/malfunction, and 15% are classified as human failing. Furthermore, the medium tree is found to classify misuse of the source of ignition (the 2nd column-row), mechanical/electrical failure/malfunction (the 4th column-row), and human failing (the 7th column-row) with performance abilities of 76%, 73%, and 71%, correspondingly. However, the medium tree algorithm is not able to classify the misuse of material ignited (the 3rd column-row), construction, design or installation deficiency (the 5th column-row), and misuse of equipment (the 6th column-row). Furthermore, Figure 5-7 presents the results of the receiver operating characteristic (ROC) curve showing the values of the false positive rate (FPR) and the true positive rate (TPR) for the medium tree classifier. The figure also presents the overall quality of the classifier through the area under the ROC curve. The ROC curve supports the confusion matrix results as the best results, with no misclassified factors, are near the top left angle of the plot; the larger area under the curve, the better classifier performance. Furthermore, ROC shows a clear comparison among classes of the best-trained model that could be used to reconstruct a confusion matrix from any point on the ROC curve. While a confusion matrix shows that the medium tree has 76% as the best 118 performance in classifying the attribute of misuse of the source of ignition, the ROC curve shows that the algorithm has 91% of AUC in classifying mechanical/electrical failure/malfunction. 61.864.160.65554.85439.258.258.654.152.3 57.554.456.55857.1 64.260.354Figure 5-7: Performance of HEFs-based classifiers with confusion matrix and ROC curve of the medium tree classifier 1: Incendiary fires 2: Misuse of source of ignition 3: Misuse of material ignited 4: Mechanical/electrical failure/ malfunction 5: Construction, design or Installation deficiency 6: Misuse of equipment 7: Human failing 119 5.3.4.3. ANN-based regression for fire risk predictions: Fire origin and ignition sources-based human factors The predictors, including ignition source factors, elder people, young people, and lone parent, were found to be correlated with the factors and attributes responses of human error factors and fire origin. This indicates that the combination of the available ignition source systems with elder people, young people, and lone-parent leads to specific HEFs-FIs at specific fire origin location. The ANN prediction model's R and R2 results were determined using three algorithms, including LM, BR, and SCG. The R results were varying positively to a certain extent number of neurons. Figure 5-8 shows that the BR algorithm has a preference for prediction accuracy, followed by LM and SCG algorithms accordingly. The optimum value of R for the BR algorithm was found to be associated with 40 neurons; however, the optimum R values for LM and SCG were found to be at 30 neurons. The BR-based ANN model was found to be very strongly correlated with R equal to 0.85 and has an ability with an R2 value of 72% in predicting possible HEFs-FIs and their origin location based on the defined predictors. The training network of 745 fire incidents with 1000 iterations for the BR algorithm stops generalization improvement at R of 0.856 at 40 neurons. The testing results of 160 fire incidents show that R is 0.738, whereas the total R-value is 0.838. The error histogram with 20 bins shows that most of the datasets have a low error. The R values of training, validation, and testing and the all-R value at 30 neurons for the LM algorithm were found to be equal to 0.825, 0.798, 0.746, and 0.809, respectively. The R values of training, validation, and testing and the all-R value at 30 neurons for the SCG algorithm were equal to 0.796, 0.783, 0.781, and 0.792, correspondingly. ANN Simulink diagram was generated, as presented in Figure 5-9, to simulate ANN in Simulink or deploy with Simulink Coder tools. The Simulink diagram was found to be an efficient technique to visualize the possible scenarios of the responses. 120 Moreover, an advanced script, MATLAB function, and MATLAB Matrix-Only Function, as shown in the appendix of chapter 5, were generated to produce results and solve similar problems. 7.65E-017.80E-017.95E-018.10E-018.25E-018.40E-0110 20 30 40 50All R valuesNeuronsLM BR SCGFigure 5-8: Results of the BR-based ANN comparing with LM and SCG for prediction of fire origin and ignition sources-based human factors 121 Note: ISFs-FIs: Ignition Source Factors-Induced Fire Incidents, EP: Elder People, YP: Young people, LP: Lone Parent, HEFs-FIs: Human Error Factors-Induced Fire Incidents, FO: Fire Origin. MATLAB matrix only function using BR algorithm for prediction of fire origin and ignition sources-based human factors function [y1] = myNeuralNetworkFunction(x1) % ===== NEURAL NETWORK CONSTANTS ===== % Input 1 x1_step1.xoffset = [0;0;3;6;4]; x1_step1.gain = [0.0202020202020202;0.222222222222222;0.08;0.25;0.333333333333333]; x1_step1.ymin = -1; % Layer 1 b1 = [1.2132056807006850185;-0.67570383438751713268;0.56176323601029742871;-2.3458836718488811357;1.2098817813073670102;0.63834177718640350285;1.8228436629593136598;-0.2138507164068512989;0.3862884906832913523;0.076368126700397609419]; IW1_1 = [-0.20672593931922875199 0.064024829200734378887 2.0131709829802990619 1.3057939107530454059 -1.0734480791654341658;1.090309305863238265 0.311567779061560024 0.59041667406424369613 0.77158931403075858846 1.2367751300817995119;-1.2947559635130252431 -0.22733809346095912307 -0.30659615908511184124 -1.0550914612297597195 0.67781529437962106943;-0.0771842025564260098 -2.0404961289442509198 -0.23830536216458433896 -0.21666513427600564423 0.33956796452186838176;1.7249783122217012821 0.28562060688623069904 0.21107488264320645177 -0.90012380429535521209 0.1128446528315439068;-0.10892712549912787756 -1.2743778072704485815 0.55614036815158984783 0.7001337578219043456 -0.26924803243015688059;-1.3574875432515902673 -2.0138850684360307675 -0.47814721292765333116 -0.40077123950911008876 -0.17594536409785127562;-0.60690918156309259235 0.3186928868389688474 -1.417889132145311315 1.5978631157923646189 -0.12011925651038019014;1.2654595818515630423 -1.2998802646746714373 0.079700717447723612441 -0.73270438916608693347 0.31008072991321938838;-2.088536573767747484 -2.1035454454645527989 0.10759526779555317333 0.31502121658345805244 -0.17261438303688908524]; % Layer 2 b2 = [-0.96151859472032852327;-0.19923005380302666323;-0.51243165312614380458;-0.21483427299964077961]; LW2_1 = [-1.0650030468625684144 -0.50883881382802853555 -0.26228612237515369632 -2.0811432296369378747 -0.76758297191078206367 2.0788604073867329447 -0.59592552025955547457 -1.0708710195840211998 -1.4059956728288183214 -0.69304189601395738141;-0.27218694689367789463 -0.18028707192088036515 -0.15734084418880928125 -0.45242143747147189892 -0.17744236000812546594 0.082622769684356334174 -Figure 5-9: Results of ANN- using BR algorithm for prediction of fire origin and ignition sources-based human factors 122 0.11465833751571836985 -0.15923929355399135677 -0.32442715191214005488 0.24482070430759422797;-0.064524911394138684262 -1.1098166567517184777 2.534944068189158628 1.0183123758219172483 1.7161342111714055214 1.6250760957349368319 -2.2919313021187992518 0.45871474348739271765 -1.2616513061588363609 -0.9506538830712154553;0.055772243192937727418 -0.028710420558326264079 0.26464112931897426639 0.27319741325986068858 0.28152944392084156 -0.09040134799234351981 -0.35984809776313148033 0.17498139051238983943 0.08036125223414582186 -0.087186866766192028244]; % Output 1 y1_step1.ymin = -1; y1_step1.gain = [0.0227272727272727;0.25;0.0202020202020202;0.25]; y1_step1.xoffset = [11;1;0;1]; % ===== SIMULATION ======== % Dimensions Q = size(x1,2); % samples % Input 1 xp1 = mapminmax_apply(x1,x1_step1); % Layer 1 a1 = tansig_apply(repmat(b1,1,Q) + IW1_1*xp1); % Layer 2 a2 = repmat(b2,1,Q) + LW2_1*a1; % Output 1 y1 = mapminmax_reverse(a2,y1_step1); end % ===== MODULE FUNCTIONS ======== % Map Minimum and Maximum Input Processing Function function y = mapminmax_apply(x,settings) y = bsxfun(@minus,x,settings.xoffset); y = bsxfun(@times,y,settings.gain); y = bsxfun(@plus,y,settings.ymin); end % Sigmoid Symmetric Transfer Function function a = tansig_apply(n,~) a = 2 ./ (1 + exp(-2*n)) - 1; end % Map Minimum and Maximum Output Reverse-Processing Function function x = mapminmax_reverse(y,settings) x = bsxfun(@minus,y,settings.ymin); x = bsxfun(@rdivide,x,settings.gain); x = bsxfun(@plus,x,settings.xoffset); end With respect to the first research question to identify important HEFs-FIs in SG-MURBs and analyze their relative frequencies; it was found that twenty-eight human error factors cause fires in SG-MURBs. These HEFs-FIs were found to be classified under seven main categories, including incendiary fires, misuse of source of ignition, misuse of material ignited, mechanical/electrical failure/malfunction, and human failing. This investigation makes it easy and effective in defining the appropriate safeguards. Also, these results help to define specific policies for each category and for a specific attribute to enhance fire safety in SG-MURBs. This investigation is comprehensive and the first of its kind to increase the awareness of people and help define preventive fire safety strategies accordingly. This identification supports the International Fire Code in requiring maintaining ignition sources [292]. 123 The second question in this study sought to develop a benchmark to evaluate the relative frequencies of HEFs-FIs that would help determine the most critical HEFs-FIs in SG-MURBs. It can be seen from the results that the most critical HEFs-FIs were determined quantitatively using a percentage from 0% to 100% helping the decision-makers to prioritize and take immediate responses. This supports Somson and Khalid's claim that incorporating human errors offers insights and recommendations to control probabilistic risk analyses [107]. HEFs-FIs with a probability of more than 70% is defined as the highest critical attributes under VH grade. Using the defined grades from VL to VH helps define the attributes that should be avoided, minimized, or transferred to other parties, such as insurance companies. These criticality results could also be important for investment-based decision-making besides the required time and cost of fire safeguards, supporting the International Fire Code [298]. This investigation supports fire and building codes, including Fire Code Canada and Building Code of Australia, where investigation of HEFs-FIs helps assess and control sources of ignition [435]. Besides, this investigation is required in enhancing safety measures [436], especially in strengthening prevention activities to control potential fire hazards [437]. The third question sought to assess the contribution of HEFs-FIs to the regional fire incidents under data uncertainties; this study used evidential reasoning to assess the HEFs-FIs effectively at a province-level to help decision-makers developing best fire safety practices. This assessment helps to have a clear set of fire safety policies and procedures to reduce the risk to as low as reasonably practicable. The assessment helps decision-makers select the necessary fire precautions and management to ensure people's safety in SG-MURBs. This effective assessment of the criticality attributes helps to specify the attributes' review time, as it varies from months to years, in the fire policies and procedures. This supports Miller's claim 124 that effective fire safety and prevention strategies require related variables of human errors [108]. Regarding the fourth question, three predictors were found to be correlated with the fire origin and ignition sources-based human factors. The fire incidents were found to be associated with medium and high levels of all the predictors. Furthermore, according to the results, installing an effective and efficient extinguishing system, such as extinguishers, at a kitchen could help to control 65% of fire incidents. This is strong evidence to improve the national fire code-Canada to include the kitchen as one of the major areas to install extinguishers. The current fire code mentions "portable extinguishers shall be located in or adjacent to corridors or aisles that provide access to exits". Furthermore, the means of egress were found to have just 6% of the occurred fire incidents. In addition, the results of this study support the National Fire Protection Association, which requires installing extinguishers, specifically near the functioning area. Moreover, specific responses could be predicted based on specific predictors in a specific building effectively using the ANN-based generated functions and simulated efficiently with the Simulink. Additionally, in this study, in terms of the model performance, ANN models were found to outperform classifiers, which supports the claim of Jinchang [449]. 5.4. Summary This chapter assessed, integrating different techniques, HEFs-FIs in SG-MURBs. Based on the critical review of fire incidents, twenty-eight HEFs-FIs as basic attributes were identified under six main attributes to contribute to fire incidents in SG-MURBs. Fitted distribution was used in order to find the appropriate fit distribution function and determine the relative frequency range of the HEFs-FIs. Then, a benchmark was developed for HEFs-FIs. The most critical 125 HEFs-FIs were identified using the benchmarking analysis. Nine HEFs-FIs were the most common and critical HEFs-FIs that all the cities should be well taken into consideration for prevention and protection strategies. The nine HEFs-FIs were incendiary fire, misuse of material ignited, mechanical/electrical failure/malfunction, equipment unattended, misuse of the source of ignition, misuse of equipment, vandalism/mischief, suspicious, and smoker’s material. Based on the analyzed and assessed results for the general attribute at BC province-level based on criticality grade, 20.68% of the HEFs-FIs are VH, 7.07% H, 5.46% M, 7.18% L, and 59.61% VL. The BR-based ANN model was very strongly correlated with R equal to 85% and ability with an R2 value of 72% in predicting possible HEFs-FIs and their origin location based on the defined predictors. Moreover, ANN models outperform the classifiers. The results help decision-makers take actions accordingly for prevention and protection purposes through education and awareness of residents and the installation of suppression materials. 126 CHAPTER 6: FIRE RISK ASSESSMENT AND PROTECTION 6.1. Background Different methods of fire control (MFC) have been used to reduce fire impacts [41][440], such as sprinklers, automatic fire suppression systems, and extinguishers using various fire suppression materials (FSMs) [127]. Five main FSMs are generally used for extinguishing fires, including water, foam, dry-powder, CO2, and wet-chemical [119]. The fire suppression foam contains fluorochemical surfactants that help control water's physical properties, allowing it to float and spread across the hydrocarbon liquid surface. The dry-powder is based on mono-ammonium phosphate (dry-chemical), whereas the wet chemical is a pressurized solution of alkali salts. Those FSMs are developed to inhibit the combustion of different CMs-FIs, including solids, liquids, gases, metals, and electrical fires [243]. Selecting appropriate FSMs for a given situation shall be determined by consideration of (i) type of fire, (ii) hazards area, and (iii) energized electrical equipment in the vicinity of the fire [119]. Otherwise, adverse impacts could be caused by using inappropriate FSM. No study is found in the published literature to select appropriate FSMs and MFC for SG-MURBs. Furthermore, no model is developed yet to predict fire impacts, including damages of fire, fire extension, and dollar loss based on CMs-FIs and MFC. Different techniques might be used for prediction, such as regression analysis and artificial neural network (ANN)[424]; whereas ANN appears to be the most suitable technique for this purpose. This study intends to answer four critical questions to enhance fire protection in SG-MURBs: (i) What are the potential CMs-FIs in SG-MURBs? (ii) How to evaluate CMs-FIs quantitatively to determine the most critical factors? (iii) What are the most appropriate FSMs and MFC for SG-MURBs? (iv) How to predict fire impacts based on CMs-FIs and MFC effectively and efficiently? To fill the 127 knowledge gap and answer the questions mentioned above, an assessment, selection, and prediction-based approach is developed. 6.2. Methodology This study proposed an approach (Figure 6-1), to assess CMs-FIs in SG-MURBs and determine the appropriate fire suppression materials and methods of fire control accordingly. In the context of this study, CM-FIs refer to the actual ignitable materials which produce the condition of fire. The approach has four main steps: (i) identify the potential CMs-FIs in SG-MURBs and analyze their relative frequencies with the related dollar loss; (ii) develop benchmarks in terms of the relative frequencies and the related dollar loss to evaluate CMs-FIs; (iii) analyze FSMs and MFC to select the appropriate methods for SG-MURBs; (iv) develop ANN predictive model for future fire impacts based on CMs-FIs, fire suppression materials and methods of fire control. The ANN model is developed to predict impacts of fire incidents as response variables, including damage extension (DE), fire extension (FE), building dollar loss (BDL), content dollar loss (CDL), and total dollar loss (TDL) based on the predictor variables CMs-FIs and methods of fire control. DE refers to the total extent of damage caused by actual burning, and also it includes damage caused by heat, smoke, water, and other extinguishing agents. FE refers to the actual extent of burning without including browning or blistering of paint. BDL estimates the building damage caused by the fire. CDL estimates the loss of the contents of the property in dollars. Whereas TDL estimates the total loss includes damage to property (BDL) and contents (CDL). The variables have specific attributes presented in Table 6-1. Besides, develop a Simulink model for an efficient analysis of all possible scenarios. The developed approach is applied to seven cities in BC using real- 128 world data to evaluate the effectiveness of the fire risk assessment approach in SG-MURBs. Details of the developed approach are presented in the following sub-sections (6.2.1)-(6.2.4). IdentificationCategorization Analysis EvaluationSelectionPredictionF1F2F3F.FnF4GroupsBUILDING COMPONENTS FURNITURECLOTHINGPAPER PRODUCTS FLAMMABLE LIQUIDSFLAMMABLE GASES CHEMICALS, PLASTICSFORESTRY PRODUCTS ELECTRICAL EQUIPMENTMISCELLANEOUSGreen Combustible materialsInvestigate all potential combustible material factors in SG-MURBsRelative frequency and impactsFitting distributionPercentiles of the probability distributions Levels identification using benchmarksCriticality evaluationComparison assessmentAppropriate fire suppression materials Methods of fire controlPerformanceTraining stateError histogramRegressionAttributesSimulation ANN-based modellingCommonCombustible materialsSmartCombustible materialsDevelop ANN modelPredict the potential fire impactsIdentify the possible combustible materialsHelp identify the method of fire control accordinglyDevelop benchmarks for combustible materialsDefine the potential factors based on frequency and impactIdentify the critical factorsCompare the factors in the city and among the citiesUpdate Fire Safety RegulationsSelect specific strategies for an effective and efficient fire safety Fire risk assessmentFire protection Attributes of DE Damage extension Attributes of FE Fire extension Dollar Loss 1 Confined to part of room/area of origin 1 Confined to object of origin Levels BDL CDL TDL 2 Confined to a room of origin 2 Confined to part of room/area of origin VL $ 5,000.00 $ 10,000.00 $ 20,000.00 3 Confined to the floor level of origin 3 Confined to room of origin L $ 50,049.94 $ 22,625.84 $ 75,175.78 4 Confined to the building of origin 4 Confined to floor level of origin M $ 95,099.87 $ 35,251.69 $ 130,351.56 5 Extended beyond the building of origin 5 Confined to building of origin H $ 20,047,549.94 $ 5,017,625.84 $ 25,065,175.78 6 Confined to roof 6 Extended beyond building of origin VH $ 40,000,000.00 $ 10,000,000.00 $ 50,000,000.00 7 Not applicable (includes vehicle, outside area) 7 Confined to roof The levels were determined based on a moving average, analyzing fire impacts of the full data set considering a series of averages of different subsets, as follows [(max) | average (max, average) | average | average (average, min)| average (average (average, min), min)], respectively. Figure 6-1: Fire protection approach for Smart-Green Multi-Unit Residential Buildings Table 6-1: Detailed attributes for impacts of fire incidents 129 6.2.1. Categorization and identification of potential CMs-FIs The occurred fire incidents in SG-MURBs are investigated using a national fire information database to identify potential factors; These factors are categorized under ten attributes [32], taking into consideration green combustible materials (GCMs-FIs) and smart combustible materials (SCMs-FIs) (Figure 1). This investigation and categorization help to enhance fire protection strategies, such as avoiding or removing critical CMs-FI and providing the appropriate FSMs. The relative frequencies of the potential CMs-FIs are calculated for a specific city using the following equation: 𝐹𝑟𝑖 =𝑁𝑖∑ 𝑁𝑖 𝑛𝑖=1⁄ ∀𝑖 = {1,2, … , 𝑛} 𝐸𝑞(6 − 1) where 𝐹𝑟𝑖 is the relative frequency of the ith CM-FIs; Ni is the number of CM-FIs ignited in fire incidents per year in one city; and ∑ 𝑁𝑖 𝑛𝑖=1 presents the total number of ignited CMs-FIs in the same city. The relative frequencies are defined by cities in this study; however, other scales of geography, such as province or country, can be selected based on the available data. 6.2.2. Benchmarking: analysis and evaluation Benchmarking consists of the analysis and evaluation of combustible materials in SG-MURBs. Benchmarks are developed for relative frequencies and total dollar loss to compare CM-FIs among the cities for fire safety improvement purposes. By benchmarking CMs-FIs of one city with other cities, decision-makers can compare fire safety levels, determine critical factors, install appropriate fire suppression materials (FSMs), and identify innovations that would improve fire safety strategies. Fire incidents data, including relative frequency and dollar loss, from different cities, are taken into considerations in order to find the best-fitted distributions for CM-FIs. The benchmarks of CMs-FIs are developed, taking into consideration the 130 probability density function (PDF) as an analytical expression to define a probability distribution of a specific CM-FIs and standard deviation (Std Dev) to define how distributions are fitted to the recorded relative frequencies. The best-fit of a distribution is determined based on the Akaike Information Criterion (AIC) as an estimator of a developed statistical model's relative quality. A model with the lowest value of AIC is selected [426], [427]. The suitable PDF is identified for the relative frequency of each CM-FI using @RiskTM 7.6 (Palisade Corp., USA) [428]. Each distribution is divided into five grades very low (VL = 1), low (L = 2), medium (M = 3), high (H = 4), and very high (VH = 5) for the percentiles <10%, 10-30%, 30-50%, 50-70%, and >=70%, respectively. The risk levels of the potential CMs-FIs were determined for a specific city by using the following equation: 𝑅𝑖 = 𝐿(𝐹𝑟𝑖) ∙ 𝐿(𝑇𝐷𝐿𝑖) ∀𝑖 = {1,2, … , 𝑛} 𝐸𝑞(6 − 2) where 𝑅𝑖 is the risk level of the ith CM-FIs; 𝐿(𝐹𝑟𝑖) presents a level of relative frequency for factor (i); and 𝐿(𝑇𝐷𝐿𝑖) is the level of TDL for the related potential CM factor (i). The risk level for the potential factors is varying in ranges of [1 5[, [5 10[, [10 15[, [15 20[, [20 25] for VL, L, M, H, and VH, respectively. 6.2.3. Selection of fire suppression materials and methods of fire control The selection of the most appropriate fire suppression materials for SG-MURBs is based on two main steps; in the first step, the suitability of fire suppression materials is determined based on the type of CMs-FIs. The most used fire suppression materials are dry-powder, foam, wet-chemical, water, and CO2 (Table 6-2). These fire suppression materials were used for different methods of fire control, including extinguishers (different sizes), sprinklers, and fire departments. In the second step, the suitability is determined based on the combustible 131 materials with high relative frequencies. All the SCMs-FIs are categorized under the group of electrical fires, and the critical ones are defined based on the developed benchmarks. Different electrical-based features are integrated into SG-MURBs to enhance (i) security with smart access and video surveillance, (ii) smart facilities management with digital alerts, HVAC control, smart lighting, smart power management, and smart proactive maintenance of equipment, (iii) safety with smart smoke and heat detectors. These smart features integrate different aspects, including electronics, software, sensors, and actuators, to connect and exchange data, which are found to be creating a potential risk of ignition. The smart systems might not have a high combustibility rate; however, the systems are electrical-based, requiring specific fire suppression materials to be used in case of a fire with the surrounded combustible materials. However, green features are analyzed qualitatively based on nine combustibility-related criteria (Ci) [29] to select fire suppression materials effectively. The criteria include readily ignitable (C1), burns readily once ignited (C2), contributes more fuel/increased heat release rate (C3), material affects burning characteristics (C4), fast(er) fire growth rate (C5), potential for shorter time to failure (C6), failure affects burning characteristics (C7), material presents flame spread concern (C8), and may impact suppression effectiveness (C9). The combustibility level of each criterion could be low (L = 2), medium (M = 3), or high (H = 4); Numbers 2, 3, 4 are given for the levels to keep the consistency with levels of the developed benchmarks. Mean and max values for each feature are defined, and fire suppression materials are selected accordingly (Table 6-2). The selection of the appropriate methods of fire control is based on low impacts of the occurred fire incidents, including damage extension (DE), fire extension (FE), building dollar loss (BDL), content dollar loss (CDL), and total dollar loss (TDL). 132 Different methods of fire control are taken into consideration, including (1) handheld extinguisher, (2) standpipe & hose systems-building, (3) makeshift firefighting aids, including garden hose, water bucket, small water container, and sand bucket, (4) Fire department – water, (5) fire department – other than water, including foam, combination foam-dry chemical, and dry chemical, (6) sprinkler system, (7) fixed system other than sprinklers, including carbon dioxide system, dry chemical system, foam-water system, wet chemical system, (8) burned out, and (9) miscellaneous, including portable high expansion foam generator, carbon dioxide handhose system – 25 kg or more, and dry chemical system - handhose system, 25 kg or more. Class A Class B Class C Class D Class E Class F Combustible materials (e.g. chapter and wood) Flammable liquids (e.g. paint and petrol) Flammable gases (e.g. butane and methane) Flammable metals (e.g. lithium and potassium) Electrical equipment (e.g. computers and generators) Deep fat fryers (e.g. chip pans) Type of FSM Water 1 0 0 0 0 0 Foam 1 1 0 0 0 0 Dry powder 1 1 1 1 1 0 CO2 0 1 0 0 1 0 Wet Chemical 1 0 0 0 0 1 Note: 1 is used for suitability and 0 for non-suitability Notched box can be plotted for CMs-FIs with methods of fire control, damage extension (DE), fire extension (FE), building dollar loss (BDL), content dollar loss (CDL), and total dollar loss (TDL) for descriptive statistics to indicate the significance of differences between values of attributes and their spread at a glance. The box depicts numerical data of attributes through their quartiles, where just the interquartile range (IQR) presents 50% of the data. It visually covers the integrated attributes, the interquartile range, median, and mean values. Furthermore, the Notched box plot can show not only a summary of the data distribution for a specific attribute but also skew, spread and correlation between two attributes. In addition, contour Table 6-2: Suitability of extinguishers to different types of fires and their possible impacts 133 plots are plotted to show the relationship between response variables, damage extension (DE), fire extension (FE), building dollar loss (BDL), and total dollar loss (TDL), and two predictor variables (CMs-FIs and methods of fire control). The contour plot can be used to investigate desirable response values with different attributes of predictors, CMs-FIs and methods of fire control, on the x and y-axes, respectively. Whereas the contour lines connect data points with the same response-value creating common ranges that present different regions showing a three-dimensional surface on a two-dimensional plane. The contour ranges help predict the type of response in case a specific method of fire control is applied for a specific combustible material. 6.2.4. Predicting the impacts of fire incidents The Artificial Neural Network (ANN) model is developed using MATLAB nprtool [430] to predict fire impacts (FIs) (response variables), including damage extension, fire extension, building dollar loss, content dollar loss, and total dollar loss based on two main predictors CMs-FIs and methods of fire control. Lavenberg-Marquardt (LM) algorithm [431] is selected based on its ability to make efficient models, be used for the ANN model to learn from 6615 data, recognize patterns, and forecast FIs in SG-MURBs. The percentages of the data used for training, validation, and testing are 70% (4631 samples), 15% (992 samples), and 15% (992 samples), respectively. The model's performance is determined using mean squared error (MSE), correlation coefficient (R) and determination coefficient (R2). The rule of thumb is used to determine the strength of the model[432]; very weak (VW), weak (W), moderately strong (MS), strong (S), very strong (VS), or perfect (P) that corresponds the R values of <0.2, < 0.4, < 0.6, < 0.8, < 1, 𝑜𝑟 1, respectively. Scaled Conjugate Gradient (SCG) and Bayesian Regularization (BR) backpropagation algorithms, as also known algorithms, are 134 taken into consideration to compare the results with LM to determine the best algorithm with prediction accuracy. An advanced script is generated to produce results and solve similar problems reusing the sequences of commands. Furthermore, MATLAB Matrix-Only Function is produced as a code generation-based deployable solution of the trained ANN. Moreover, the Simulink model is generated to simulate ANN efficiently. In addition to the function fitting neural network, different components are integrated for efficient, robust system development, and visual assessment of the predictors. Concerning the predictors, a sine wave component is used to generate and describe a smooth periodic oscillation of fifteen possible scenarios on a dashboard scope. The type of attributes (CMs-FIs and methods of fire control) of the fifteenth scenario are presented using half gauge components. Regarding the responses, the results of the fifteen scenarios are presented using dashboard scopes. Whereas gauge components are used to visualize precisely attributes (fire extension and damage extension) and levels (building dollar loss, content dollar loss, and total dollar loss) of the last scenario. 6.3. Results and Discussions This section presents the results and discussions of the performed assessment for the combustible materials in SG-MURBs in order to select and use effective fire suppression materials and methods of fire control in a systematic manner. It interprets and describes the significance of the findings explaining new understanding and insights into the results. 6.3.1. Potential combustible materials-induced fire incidents The investigation of the fire incidents that occurred in the seven cities found that thirty-six CMs-FIs to be potential factors in SG-MURBs. The factors were categorized under ten 135 attributes to help select the appropriate fire suppression materials accordingly. The relative frequencies of the potential CMs-FIs for the seven cities were determined and presented in Table 6-3. Cooking oil and trash were the factor having the highest relative frequencies in all the cities. Taking into consideration the highest relative frequencies of the attributes, the building components attribute was found to be the most potential CMs-FIs in Vernon with a relative frequency equal to 1.33E-01 per year. The attribute of furniture was the most potential CMs-FIs in West Kelowna, Surrey, and Vancouver with 2.26E-01, 1.20E-01, and 1.12E-01, respectively. The attribute of wood was found to be the most potential CMs-FIs in Victoria and Surrey with 1.34E-01 and 1.12E-01, respectively. The attribute of flammable liquids was the most potential CCMs-FIs in Kelowna, Vernon, and Victoria with 2.22E-01, 2.00E-01, and 1.42E-01, respectively. It can be seen that the relative frequencies of the most potential CMs-FIs vary significantly from one city to another. Consequently, fire protection strategies should be enhanced accordingly, such as avoiding or removing critical CMs-FI and installing the appropriate fire suppression systems. The results of the investigation show that the cities have recorded different relative frequency for the fire confined to electrical equipment, 2.08E-02, 3.17E-02, 3.01E-02, 00E-00, 4.44E-02, 3.97E-02, and 4.23E-02, with Kamloops, Kelowna, Surrey, Victoria, Vernon, Vancouver, and West Kelowna, respectively. Figure 2 shows fifty-one green features that might be integrated into SG-MURBs under seven main attributes. Some of the features could have a high combustibility level; therefore, analyzing these features could help in installing effective fire suppression materials accordingly. Furthermore, the results aid in avoiding the usage of green features with a high combustibility level. 136 Combustible materials (CMs-FIs) Factors Frequencies/year Kamloops Kelowna Surrey Victoria Vernon Vancouver West Kelowna BUILDING COMPONENTS Attribute 1 Roof covering 1 0.00E+00 0.00E+00 1.00E-02 0.00E+00 0.00E+00 2.65E-03 0.00E+00 Exterior wall covering, surface, finish - includes eave (prevents water from leaking into the home) 2 2.08E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Exterior trim and appurtenance (includes door, porch, platform, balcony) 3 0.00E+00 1.59E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Floor covering - carpet, rug 4 6.94E-03 3.17E-02 1.00E-02 1.67E-02 2.22E-02 1.15E-02 1.41E-02 Building component - floor, ceiling, roof (excludes floor covering, ceiling covering) 5 6.94E-03 0.00E+00 1.67E-03 0.00E+00 4.44E-02 5.30E-03 1.41E-02 Building component – wall (excludes exterior wall, interior wall covering) 6 6.94E-03 1.59E-02 1.51E-02 0.00E+00 4.44E-02 3.09E-02 2.82E-02 Interior wall covering (excludes plastic) 7 6.94E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Insulation - wood, wood product - rigid (includes cellulose insulation; excludes wood shavings, sawdust) 8 6.94E-03 0.00E+00 3.34E-03 1.67E-02 2.22E-02 6.18E-03 1.41E-02 Insulation - mineral (includes binder, envelope) 9 6.94E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 FURNITURE, FURNISHINGS Attribute 2 Furniture - upholstered (includes chesterfield, sofa, davenport, couch) 10 3.47E-02 7.94E-02 4.85E-02 3.33E-02 2.22E-02 5.91E-02 1.13E-01 Furniture - wood - not upholstered (includes a table, bookcase, desk, chest of drawers, cabinet, dresser, filing cabinet, piano, organ, not upholstered chair) 11 6.94E-03 0.00E+00 1.67E-03 0.00E+00 0.00E+00 4.41E-03 0.00E+00 Mattress (includes hide-a-bed mattress, box spring) 12 4.86E-02 0.00E+00 4.18E-02 5.00E-02 4.44E-02 2.12E-02 8.45E-02 Bedding (includes a blanket, sheet, comforter, eiderdown, heating pad, pillow) 13 1.39E-02 1.59E-02 2.01E-02 0.00E+00 0.00E+00 1.77E-02 1.41E-02 Drapery (includes curtain, blind, tapestry) 14 0.00E+00 0.00E+00 8.36E-03 0.00E+00 0.00E+00 9.71E-03 1.41E-02 Broom, mop, brush 15 6.94E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 CLOTHING, TEXTILES Attribute 3 Clothing - cotton 16 1.39E-02 3.17E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Clothing - synthetic fibre - includes mixed fibres 17 0.00E+00 1.59E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Fabric - cotton (includes finished goods; excludes fibre, yarn, clothing, bedding, drapery) 18 6.94E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Fabric - synthetic fibre (includes finished goods; excludes fibre, yarn, clothing, bedding, drapery) 19 6.94E-03 0.00E+00 1.17E-02 0.00E+00 0.00E+00 1.68E-02 1.41E-02 WOOD, CHAPTER PRODUCTS Attribute 4 Wood (includes box, barrel, case, finished lumber, plywood; excludes structural component, furniture, furnishings and felled timber) 20 2.08E-02 0.00E+00 1.51E-02 1.67E-02 0.00E+00 1.77E-02 1.41E-02 Chapter, packing material (includes shredded chapter) 21 0.00E+00 1.59E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Wastechapter (excludes garbage, trash classified) 22 2.08E-02 3.17E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Cardboard (includes carton, box) 23 2.08E-02 3.17E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Chapter stock (includes stationery supplies, files, book, magazine, newschapter, writing chapter) 24 6.94E-03 1.59E-02 9.70E-02 1.17E-01 4.44E-02 7.77E-02 8.45E-02 FLAMMABLE LIQUIDS, COMBUSTIBLE LIQUIDS Attribute 5 Gasoline 25 1.39E-02 0.00E+00 5.02E-03 0.00E+00 0.00E+00 6.18E-03 0.00E+00 Diesel 26 0.00E+00 0.00E+00 1.67E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Cooking oil, fat 27 9.72E-02 2.22E-01 1.35E-01 1.67E-01 2.00E-01 1.31E-01 9.86E-02 Tar, asphalt (includes pitch, bitumen) 28 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.77E-03 0.00E+00 FLAMMABLE GASES Attribute 6 Natural gas 29 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4.41E-03 1.41E-02 Propane (includes butane, liquefied petroleum gases) 30 0.00E+00 0.00E+00 3.34E-03 1.67E-02 2.22E-02 1.06E-02 1.41E-02 CHEMICALS, PLASTICS, METALS Attribute 7 Plastics (excludes cellulose nitrate) 31 3.47E-02 3.17E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 AGRICULTURAL, FORESTRY PRODUCTS Attribute 8 Grass, brush, leaves 32 0.00E+00 0.00E+00 1.51E-02 1.67E-02 2.22E-02 1.41E-02 0.00E+00 ELECTRICAL EQUIPMENT Attribute 9 Insulation, electric - confined to electrical equipment (includes only when confined to electrical equipment) 33 2.08E-02 3.17E-02 3.01E-02 0.00E+00 4.44E-02 3.97E-02 4.23E-02 MISCELLANEOUS Attribute 10 Coke, coal 34 0.00E+00 3.17E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Creosote (including in chimney) 35 6.94E-03 0.00E+00 0.00E+00 1.67E-02 0.00E+00 0.00E+00 0.00E+00 Garbage, trash, rubbish 36 4.86E-02 6.35E-02 1.40E-01 1.33E-01 1.56E-01 1.21E-01 1.97E-01 Table 6-3: Combustible materials in SG-MURBs with relative frequencies in seven cities 137 Green featuresExterior Materials and SystemsFaçade AttributesInterior Materials and Finishes Alternative Energy Systems Building Systems & IssuesInterior Space Attributes Structural Materials and SystemsFiber reinforced polymer (FRP) elementsLightweight concreteLightweight engineered lumberPlastic lumberBambooBio-polymer lumberExtended solar roof panels Structural integrated panel Rigid foam insulationExterior insulation & finish Spray-applied foam insulation Foil insulation systemsHigh-performance glazingLow-emissivity & reflective coating Bamboo, other cellulosic Double-skin façade / cavity walls Bio-polymersVegetative roof systems Area of glazingArea of combustible material Exterior vegetative coveringFiber reinforced polymer walls / finishesBio-polymer wall / finishes Glass wallsBio-filtration wallsWood panel walls / finishes Bamboo walls / finishes Fiber reinforced polymer walls flooringBio-polymer flooring Bamboo flooringBio-polymer lumberMore enclosed spacesHigher insulation valuesTighter constructionMore open space (horizontal) Interior vegetationMore open space (vertical)Natural ventilationHigh volume low speed fans Refrigerant materialsGrey-water for suppression On-site waste/water treatmentOn-site water treatment and cogenerationRain-water for suppressionReduced water suppression systemsTankless water heatersCogeneration systemsBattery storage systems Oil-filled PV panels PV roof panels Figure 6-2: General green features of SG-MURBs 138 6.3.2. Benchmarking Results of relative frequency-based benchmarks for the 36 CMs-FIs are presented in Table 6-4. Besides, results for dollar loss-based benchmarks, related to the same CMs-FIs, are presented in Table 6-5. It can be seen that the benchmarks for relative frequencies and dollar loss of CMs-FIs fit well in different types of PDFs. Regarding relative frequency-based benchmarks, 62% of CMs-FIs were found to be following Exponential (Expon) distribution, the rest percentage of CMs-FIs were found to be following six distributions Extvalue (8%), Levy (3%), Pareto (5%), Laplace (3%), Uniform (16%), and ExtvalueMin with 3%. The table presents the mean values of each fitted distribution of CMs-FIs followed by Std Dev. It can be seen that the Std Dev values of CMs-FIs are very low, indicating the distributions are well fitted to the recorded relative frequencies. Max values for dollar loss were taken into considerations to present the pessimistic value each CMs-FIs could reach. The tables present five percentile values for each CM-FIs at 10%, 30%, 50%, 70%, and 90% to the grades VL, L, M, H, and VH, respectively. The table presented the results of the developed benchmarks to help compare CM-FIs among the cities for fire safety improvement purposes, including identifying critical CMs-FIs for each city, analyzing and comparing fire safety performance, and collaborating with other cities. 139 Table 6-4: Best fitted distributions for relative frequencies of CMs-FIs Factors Description Mean Std Deviation <10% (VL) 10-30% (L) 30-50% (M) 50-70% (H) >=70% (VH) 1 Expon(0.0018116,RiskShift(-0.0002588)) 1.55E-03 1.81E-03 0.00E-00 3.87E-04 9.97E-04 1.92E-03 5.16E-03 2 Expon(0.0029762,RiskShift(-0.00042517)) 2.55E-03 2.97E-03 0.00E-00 6.36E-04 1.64E-03 3.16E-03 8.49E-03 3 Expon(0.0026455,RiskShift(-0.00044092)) 2.20E-03 2.64E-03 0.00E-00 5.03E-04 1.39E-03 2.74E-03 7.48E-03 4 Extvalue(0.0126371,0.0057214) 1.59E-02 7.33E-03 7.86E-03 1.16E-02 1.47E-02 1.85E-02 2.96E-02 5 Levy(-0.00017327,0.00055998) 3.36E+01 3.09E+03 3.37E-05 3.48E-04 1.06E-03 3.60E-03 1.42E-01 6 Extvalue(0.013353,0.012098) 2.03E-02 1.55E-02 3.26E-03 1.11E-02 1.78E-02 2.58E-02 4.93E-02 7 Expon(0.00099206,RiskShift(-0.00014172)) 8.50E-04 9.92E-04 0.00E-00 2.12E-04 5.46E-04 1.05E-03 2.83E-03 8 Extvalue(0.0063878,0.0060737) 9.89E-03 7.80E-03 1.32E-03 5.26E-03 8.61E-03 1.26E-02 2.44E-02 9 Expon(0.00099206,RiskShift(-0.00014172)) 8.50E-04 9.91E-04 0.00E-00 2.12E-04 5.46E-04 1.05E-03 2.83E-03 10 Pareto(1.2711,0.022222) 9.57E-02 6.90E-01 2.41E-02 2.94E-02 3.83E-02 5.73E-02 2.34E-01 11 Expon(0.0018614,RiskShift(-0.00026591)) 1.60E-03 1.86E-03 0.00E-00 3.98E-04 1.02E-03 1.97E-03 5.31E-03 12 Laplace(0.044444,0.02427) 4.44E-02 2.43E-02 1.68E-02 3.57E-02 4.44E-02 5.32E-02 8.39E-02 13 Uniform(-0.0033445,0.023411) 1.00E-02 7.72E-03 0.00E-00 4.68E-03 1.00E-02 1.54E-02 2.21E-02 14 Expon(0.0045935,RiskShift(-0.00065621)) 3.94E-03 4.60E-03 0.00E-00 9.82E-04 2.53E-03 4.87E-03 1.31E-02 15 Expon(0.00099206,RiskShift(-0.00014172)) 8.50E-04 9.91E-04 0.00E-00 2.12E-04 5.46E-04 1.05E-03 2.83E-03 16 Expon(0.0065193,RiskShift(-0.00093132)) 5.59E-03 6.52E-03 0.00E-00 1.39E-03 3.59E-03 6.92E-03 1.86E-02 17 Expon(0.0022676,RiskShift(-0.00032394)) 1.94E-03 2.27E-03 0.00E-00 4.85E-04 1.25E-03 2.41E-03 6.47E-03 18 Expon(0.00099206,RiskShift(-0.00014172)) 8.50E-04 9.93E-04 0.00E-00 2.12E-04 5.46E-04 1.05E-03 2.83E-03 19 Expon(0.007072,RiskShift(-0.0010103)) 6.06E-03 7.07E-03 0.00E-00 1.51E-03 3.89E-03 7.50E-03 2.02E-02 20 Uniform(-0.0034722,0.024306) 1.04E-02 8.02E-03 0.00E-00 4.86E-03 1.04E-02 1.60E-02 2.29E-02 21 Expon(0.0022676,RiskShift(-0.00032394)) 1.94E-03 2.27E-03 0.00E-00 4.85E-04 1.25E-03 2.41E-03 6.47E-03 22 Expon(0.0075113,RiskShift(-0.001073)) 6.44E-03 7.51E-03 0.00E-00 1.61E-03 4.13E-03 7.97E-03 2.14E-02 23 Expon(0.0075113,RiskShift(-0.001073)) 6.44E-03 7.57E-03 0.00E-00 1.61E-03 4.13E-03 7.97E-03 2.14E-02 24 Uniform(-0.011343,0.13495) 6.18E-02 4.22E-02 3.27E-03 3.25E-02 6.18E-02 9.10E-02 1.28E-01 25 Expon(0.0035834,RiskShift(-0.00051192)) 3.07E-03 3.58E-03 0.00E-00 7.66E-04 1.97E-03 3.80E-03 1.02E-02 26 Expon(0.00023889,RiskShift(-0.0000341274)) 2.05E-04 2.39E-04 0.00E-00 5.11E-05 1.31E-04 2.53E-04 6.81E-04 27 Pareto(2.566,0.097222) 1.59E-01 1.19E-01 1.01E-01 1.12E-01 1.27E-01 1.55E-01 3.12E-01 28 Expon(0.00025218,RiskShift(-0.000036025)) 2.16E-04 2.52E-04 0.00E-00 5.39E-05 1.39E-04 2.68E-04 7.19E-04 29 Expon(0.0026425,RiskShift(-0.0003775)) 2.27E-03 2.65E-03 0.00E-00 5.65E-04 1.45E-03 2.80E-03 7.54E-03 30 Uniform(-0.0037037,0.025926) 1.11E-02 8.55E-03 0.00E-00 5.18E-03 1.11E-02 1.70E-02 2.44E-02 31 Expon(0.0094955,RiskShift(-0.0013565)) 8.14E-03 9.49E-03 0.00E-00 2.03E-03 5.22E-03 1.01E-02 2.71E-02 32 Uniform(-0.0037037,0.025926) 1.11E-02 8.55E-03 0.00E-00 5.18E-03 1.11E-02 1.70E-02 2.44E-02 33 ExtvalueMin(0.036183,0.0098868) 3.05E-02 1.27E-02 1.39E-02 2.60E-02 3.26E-02 3.80E-02 4.70E-02 34 Expon(0.0045351,RiskShift(-0.00064788)) 3.89E-03 4.53E-03 0.00E-00 9.69E-04 2.50E-03 4.81E-03 1.29E-02 35 Expon(0.003373,RiskShift(-0.00048186)) 2.89E-03 3.37E-03 0.00E-00 7.21E-04 1.86E-03 3.58E-03 9.62E-03 36 Uniform(0.023849,0.22195) 1.23E-01 5.72E-02 4.36E-02 8.33E-02 1.23E-01 1.63E-01 2.12E-01 140 Table 6-5: Best fitted distributions for total dollar loss of CMs-FIs Features Description Mean Maximum <10% (VL) 10-30% (L) 30-50% (M) 50-70% (H) >=70% (VH) 1 RiskExpon(215385) $215,249.90 $1,490,941.70 $22,585.06 $76,818.21 $149,190.82 $258,796.30 $645,079.58 2 RiskExpon(368035) $367,868.02 $2,791,013.70 $38,554.94 $131,095.25 $254,755.14 $441,884.52 $1,096,501.13 3 RiskExpon(6071.4) $6,072.72 $50,199.20 $637.94 $2,164.82 $4,208.11 $7,309.36 $18,088.37 4 RiskPareto(0.25304,1000) $152,572,501,926,787.00 $152,461,479,371,385,000.00 $1,510.73 $4,090.09 $15,376.03 $115,275.02 $129,301,678.56 5 RiskExpon(970056) $969,844.09 $7,676,708.98 $101,247.02 $345,432.66 $672,044.39 $1,167,339.74 $2,888,000.70 6 RiskLevy(0,537911.9147) $4,077,483,228.21 $3,822,728,427,184.66 $131,126.62 $433,064.10 $1,114,864.68 $3,533,826.75 $136,664,831.16 7 RiskExpon(297693,RiskShift(-42527)) $257,095.23 $4,273,736.78 $11,200.14 $63,353.26 $163,279.64 $315,825.39 $845,430.20 8 RiskUniform(0,1002852) $445,226.07 $1,001,909.76 $892.00 $221,455.48 $444,793.34 $667,324.83 $946,883.96 9 RiskExpon(2142.9,RiskShift(-306.12)) $1,837.84 $18,013.41 $81.36 $458.14 $1,176.13 $2,271.76 $6,096.21 10 RiskPareto(0.42171,46750) $48,999,789,556.51 $48,463,485,992,498.50 $59,873.61 $108,767.36 $240,907.53 $808,783.21 $54,664,648.46 11 RiskExpon(74091,RiskShift(-10584)) $63,687.70 $740,440.11 $2,819.65 $15,808.06 $40,641.93 $78,606.47 $211,076.79 12 RiskUniform(0,2120389) $910,602.48 $2,119,372.94 $59,047.04 $426,588.61 $909,185.91 $1,394,332.51 $1,998,149.37 13 RiskLevy(0,29812.7686) $96,337,115.02 $81,131,622,962.77 $3,798.89 $20,480.96 $58,281.45 $192,893.56 $7,516,006.55 14 RiskExpon(134100,RiskShift(-19157)) $114,898.20 $1,056,880.97 $5,117.25 $28,601.31 $73,732.74 $142,244.55 $381,780.83 15 RiskExpon(1000,RiskShift(-142.86)) $856.06 $6,834.83 $38.35 $212.51 $548.72 $1,059.41 $2,844.85 16 RiskExpon(2244.1,RiskShift(-320.59)) $1,926.53 $20,683.77 $86.53 $477.88 $1,233.80 $2,377.45 $6,401.23 17 RiskExpon(9072.1,RiskShift(-1296)) $7,765.14 $65,454.19 $348.83 $1,933.54 $4,987.01 $9,615.04 $25,754.62 18 RiskExpon(21814,RiskShift(-3116.3)) $18,707.04 $175,659.11 $826.16 $4,640.73 $11,979.52 $23,075.41 $62,190.13 19 RiskPareto(0.22321,650) $43,743,592,240,286,100.00 $43,740,216,431,478,500,000.00 $1,040.75 $3,194.65 $14,489.23 $141,242.00 $426,799,340.69 20 RiskPareto(0.50714,80200) $159,015,395.77 $69,111,759,472.79 $98,678.53 $161,605.42 $313,800.10 $857,387.66 $29,052,923.12 21 RiskExpon(55471,RiskShift(-7924.5)) $47,592.70 $441,997.96 $2,096.87 $11,801.89 $30,497.37 $58,779.71 $157,949.45 22 RiskExpon(55087,RiskShift(-7869.6)) $47,196.80 $384,930.30 $2,126.51 $11,758.01 $30,216.33 $58,316.96 $156,684.75 23 RiskExpon(330088,RiskShift(-47155)) $283,238.54 $2,691,707.47 $12,441.65 $70,428.86 $181,511.67 $349,284.26 $935,268.65 24 RiskPareto(0.35701,20205) $12,590,762,239.27 $10,591,433,594,149.20 $27,093.65 $54,681.80 $140,387.34 $584,444.97 $84,343,067.20 25 RiskPareto(0.47681,52100) $19,131,592,788.13 $19,057,645,179,677.60 $64,974.17 $110,060.11 $222,402.12 $649,032.97 $27,502,566.51 26 RiskLevy(0,934.6728) $1,739,638.49 $926,989,477.65 $54.51 $578.88 $1,760.93 $5,964.72 $228,335.86 27 RiskPareto(0.50273,133900) $302,318,475.24 $198,936,194,821.09 $165,057.24 $272,161.80 $530,138.47 $1,464,308.14 $51,198,117.53 28 RiskExpon(226264,RiskShift(-32323)) $193,683.33 $1,531,401.12 $8,621.13 $48,058.52 $124,333.07 $239,921.56 $641,658.19 29 RiskExpon(86747,RiskShift(-12392)) $74,488.11 $788,211.48 $3,264.83 $18,472.04 $47,633.60 $91,769.33 $246,391.22 30 RiskLevy(0,2623.9664) $631,165,924.08 $629,467,358,539.54 $345.85 $1,816.11 $5,134.84 $16,935.32 $658,968.57 31 RiskExpon(178261,RiskShift(-25465)) $152,756.33 $1,245,459.37 $6,827.34 $37,980.79 $98,029.30 $188,904.63 $506,072.85 32 RiskLevy(0,36491.8325) $50,051,991.40 $26,560,169,523.17 $4,244.64 $24,795.61 $70,753.62 $235,470.81 $9,224,680.86 33 RiskPareto(0.29177,16300) $3,838,257,529,086.67 $3,747,283,240,514,460.00 $23,305.94 $55,079.16 $174,641.81 $1,006,723.87 $467,634,112.93 34 RiskExpon(423677,RiskShift(-60525)) $362,824.62 $2,984,710.83 $16,187.10 $90,416.88 $232,626.04 $448,918.19 $1,205,643.90 35 RiskExpon(151477,RiskShift(-14339)) $137,341.72 $1,456,416.94 $1,555.43 $39,519.76 $90,595.91 $167,652.28 $437,519.30 36 RiskPareto(0.19947,1200) $5,580,805,359,448,990.00 $5,423,939,444,073,230,000.00 $2,025.21 $7,165.62 $38,670.64 $499,910.64 $3,777,880,555.71 141 6.3.3. Evaluation of CMs-FIs Figure 6-3 presents the relative frequency-based combustibility levels, VL(1), L(2), M(3), H(4), and VH(5), of CMs-FIs based on the developed benchmarks (Table 6-4) for the randomly selected city, Vernon. The figure shows that the levels of CMs-FIs are varying from one city to another. Therefore, each city should consider specific fire protection strategies for critical CMs-FIs. Furthermore, the figure shows the percentages of the combustibility levels in each city. The most critical CMs-FIs (Figure 6-3) for each city with VH and H frequency were determined; 41% of CMs-FIs in Kamloops were VH, and 8% were H. Kelowna has 32% (VH) and 0% (H), whereas Surrey has registered 22% (VH) and 19% (H). In Victoria, 14% of CMs-FIs (VH) and also 14% (H). Vernon has 22% (VH) and 5% (H). Vancouver registered 38% (VH) and 8% (H). Lastly, West-Kelowna has 27% (VH), and 11% of CMs-FIs are VH. Levels of relative frequencies (Fr(i)) and total dollar loss (TDL(i)) related to specific CMi-FIs are presented in Table 6-6. Furthermore, risk levels (R(i)) for each CMi-FIs were determined using equation (6-2). Kamloops was the critical city with the highest percentage (47%) of CMs-FIs with VH-risk level. Vancouver followed it with 42% of CMs-FIs having VH-level, then Surrey, Kelowna, West Kelowna, Vernon, and Victoria with 33%, 28%, 8%, 6%, and 6%, respectively. In BC, 24% of CMs-FIs were found to be having a VH-risk level, 5% were H, 13% were M, 12% L, and 47% of the CMs-FIs were VL. Risk levels of SCMs-FIs were found to be different from one city to another; Vancouver and West-Kelowna were found to have VH, Surrey (H), Kamloops (M), Kelowna, Vernon, and Victoria have L level. Figure 6-4 shows the green features' analysis from the combustibility perspective, taking into account the mean and max values of the ninth criteria. The figure shows, taking into consideration the significant results in Table A-6-1 (appendix), respectively 13% and 3% of the green features 142 were found under M and H level for readily ignitable criteria (C1). Regarding the criteria of burns readily once ignited (C2), 17% were M and14% were H. Contributes more fuel/increased heat release rate (C3) has 9% M, and 28% H. Material affects burning characteristics (C4) has found to have 16% M and 7% H. Fast(er) fire growth rate (C5) has 16% M and 4% H. Potential for shorter time to failure (C6) has found to have 0% of M and 7% H. For failure affects burning characteristics (C7), 4% M and 7% H. Material presents flame spread concern (C8) has 6% M and 28% H. Lastly, may impact suppression effectiveness (C9) has 32% of green features M and 1% H. Taking into consideration the max values, 45% of the green features have a high combustibility level. Consequently, these results assist management in allocating methods of fire control with appropriate fire suppression materials and understand risk/benefit trade-offs. 123451 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38Combustiblility levelCMs-FIsVernon0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%KamloopsKelownaSurreyVictoriaVernonVancouverWest KelownaVL L M H VH Figure 6-3: Percentages and levels of relative frequency-based CMs-FIs in the cities 143 Table 6-6: Levels of CMs-FIs for relative frequency, total dollar loss, and risk levels for seven cities CMs-FIs Kamloops Kelowna Surrey Vancouver Vernon Victoria West Kelowna Kamloops Kelowna Surrey Vancouver Vernon Victoria West Kelowna Fr(i) TDL(i) Fr(i) TDL(i) Fr(i) TDL(i) Fr(i) TDL(i) Fr(i) TDL(i) Fr(i) TDL(i) Fr(i) TDL(i) R(i) R(i) R(i) R(i) R(i) R(i) R(i) 1 L VL L VL VH VH VH VH L VL L H L VL VL VL VH VH VL L VL 2 VH VH L VH L VL L VL L VL L VL L VL VH M VL VL VL VL VL 3 L VH VH VH L VL L VL L VL L VL L VL M VH VL VL VL VL VL 4 VL VH VH VH L L L H VH VL H VL M VL L VH VL L L VL VL 5 VH VH VL M H VH VH VH VH VL VL VL VH VL VH VL VH VH L VL L 6 L H M H M VH VH VH VH VH VL L VH VH L M H VH VH VL VH 7 VH VH L M L VL L VL L VL L VL L VL VH L VL VL VL VL VL 8 M H VL H L VH M VH VH L VH L VH L M VL M H M M M 9 VH VH L L L VL L VL L VL L VL L VL VH VL VL VL VL VL VL 10 M VH VH VH H VH VH VH VL H M H VH L H VH VH VH VL M M 11 VH VH L VH H VL VH H L VL L VL L VL VH M VL VH VL VL VL 12 H VH VL L M VH L VH H VH H M VH VL VH VL H M VH M L 13 H VH VH VH VH VH VH VH L VL L VL H L VH VH VH VH VL VL L 14 L VH L L VH H VH M L VL L VL VH VL M VL VH H VL VL L 15 VH VH L L L VL L VL L VL L VL L VL VH VL VL VL VL VL VL 16 VH VH VH VH L VL L VL L VL L VL L VL VH VH VL VL VL VL VL 17 L VH VH VH L VL L VL L VL L VL L VL M VH VL VL VL VL VL 18 VH VH L L L VL L VL L VL L VL L VL VH VL VL VL VL VL VL 19 H VH L VH VH VH VH VH L VL L VL VH VL VH M VH VH VL VL L 20 VH VH L L H VH VH VH L VH VH VH H M VH VL VH VH M VH M 21 L VH VH L L VL L VL L VL L VL L VL M M VL VL VL VL VL 22 VH VH VH VH L VL L VL L VL L VL L VL VH VH VL VL VL VL VL 23 VH VH VH H L VL L VL L VL L VL L VL VH VH VL VL VL VL VL 24 L H L VL VH VH H VH M M VH M H VH L VL VH VH L H VH 25 VH VH L VH VH VH VH VH L L L L L H VH M VH VH VL VL L 26 L VL L VL VH M L VH L L L VL L VL VL VL H M VL VL VL 27 VL VH VH H H VH H VH VH M VH VH VL H L VH VH VH H VH VL 28 L L L L L L VH VH L VL L L L VL VL VL VL VH VL VL VL 29 L VH L L L VL VH VH L VL L VL VH VL M VL VL VH VL VL L 30 L VH L H L H M VH VH VL H VL H VL M L L H L VL VL 31 VH VH VH M L VL L VL L VL L VL L VL VH H VL VL VL VL VL 32 L H L VH H VH H VH VH L H VL L VL L M VH VH M VL VL 33 L VH M L M VH VH VH VH VL VL VH VH VH M L H VH L L VH 34 L VH VH VH L VL L VL L VL L VL L VL M VH VL VL VL VL VL 35 VH VH L VH L M L H L VL VH L L VL VH M L L VL M VL 36 L VH L VH H VH M VH H L H L VH VL M M VH H L L L 144 6.3.4. Selection of fire suppression materials and methods of fire control In this section, the best fire suppression materials are selected based on combustible materials. Furthermore, methods of fire control are analyzed to be selected effectively. 6.3.4.1. Selection of appropriate fire suppression materials The appropriate fire suppression materials for CMs-FIs in SG-MURBs are determined (Figure 6-5) using the suitability of fire suppression materials (Table 6-2) and mean values of relative frequencies. Water was found to be appropriate for 73% of CMs-FIs, whereas foam, dry-powder, CO2, and wet-chemical for 89%, 100%, 16%, and 76%, respectively. As Figure 6-5 40312835 37464328 299611 11042128175 3 5 5191C1 C2 C3 C4 C5 C6 C7 C8 C9NUMBER OF GREEN FEATURESPOTENTIAL HAZARDSL M H12341 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51Combustibility levelsGreen featuresMean MaxFigure 6-4: Green features and their combustibility levels 145 presents, investigating the relative frequency of combustible materials helps to prioritize the type of fire suppression materials that should be installed. The appropriate fire suppression materials using the investigated relative frequencies for CMs-FIs are different from one city to another due to the difference in relative frequencies of ignited combustible materials in these cities. The most appropriate FSMs for the top 10 critical CMs-FIs are determined; water is appropriate for 70% pf CMs-FIs, foam (80%), dry-powder (100%), CO2 (20%), and wet-chemical (70%). 1.00E-041.00E-031.00E-021.00E-011.00E+0001FrSuitabilityDry powder Wet Chemical Fr (Mean)1.00E-041.00E-031.00E-021.00E-011.00E+00011 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36CMs-FIsFrSuitabilityWater Foam CO2 Fr (Mean) Figure 6-5: Appropriate fire suppression materials for CMs-FIs in SG-MURBs 146 In contrast, dry-powder and CO2 are the only appropriate fire suppression materials (100%) to fight fires with smart features. The appropriate fire suppression materials (FSMs), with normalized percentages, for high combustibility level of the GCMs-FIs are as follows (Figure 6-6): dry-powder was appropriate for 28% of GCMs-FIs followed by foam (22%), wet chemical (22%), water (22%), and finally CO2 was suitable for just 5% of GCMs-FIs. Dry-powder, foam, wet chemical, and water suppression materials were appropriate equally for all the GCMs-FIs with medium combustibility levels; however, CO2 was suitable for just 1% of GCMs-FIs. Whereas dry-powder was appropriate for 29% of GCMs-FIs with low combustibility level followed by foam, wet chemical, and water with 21%, and finally, CO2 was suitable for just 8% of GCMs-FIs. Taking into consideration the mean of all the combustibility levels of GCMs-FIs, the appropriate suppression materials for the GCMs-FIs are as follows: dry-powder was appropriate for 27% of GCMs-FIs followed by foam, wet-chemical, and water with 23%, and finally, CO2 was appropriate for just 5% of GCMs-FIs. It can be seen that the percentages of appropriate fire suppression materials for GCMs-FIs with high combustibility levels are almost equal, with a difference of 3.10E-3, to the mean percentages of all three levels. Therefore, using fire suppression materials for high combustibility level-based, GCMs-FIs was appropriate for the rest of GCMs-FIs. 147 6.3.4.2. Selection of the appropriate methods of fire control Figure 6-7 presents the Notched box plot for different attributes of CMs-FIs with methods of fire control, fire damage, fire extension, and dollar losses (BDL, CDL, and TDL). The figure presents descriptive statistics in a graphic way of 6615 data usefulness; it shows that all the attributes do overlap and are drawn from the same population. All the attributes present a specific boxplot for the data distribution, except CMs-FIs with fire extension, building dollar loss, and total dollar loss for the attributes 7, 5, and 5, respectively, where just a few data are noticed. The figure helps to compare the range and distribution of the mean for the variables’ attributes. Consequently, it can be noticed that there is a more significant variability for specific attributes than others with no outliers. Besides the skew and spread of the attribute’s 0%5%10%15%20%25%30%35%0 1 2 3 4 5 6PercentageType of enxtinguishing materialsH M L Mean(L,M,H)CO2 Foam D-powder W-Chemical Water Figure 6-6: Assessment of appropriate extinguishing systems for GCMs-FIs 148 data, the figure shows a correlation with no outliers between the two attributes. This correlation helps to control more than 50% of the attributes’ relationships. In addition, Figure 6-8 presents contour plots to show the relationship between response variables fire extension, fire damages, and dollar losses (BDL and TDL) and two predictor variables; CMs-FIs and methods of fire control. The figure shows dangerous levels of response variables that should be given investigation priority. Regarding the fire extension and fire damages, it can be noticed that their highest level, extension and damages extended beyond the building of origin, are recorded with fires of flammable liquids and gases where standpipe and hose systems are used, water-based systems. This confirms that using inappropriate fire suppression materials endangers the situation instead. The second most severe scenario, the extend of fire and damages to the building, is related to the intervention fire department with all types of CMs-FIs. The figure shows that most of the damage cases are the damages extended to floor level and are mainly correlated with standpipe, hose systems, and sprinkler systems. The figure shows that the high dollar loss is mainly related to a standpipe, hose systems of a building, firefighting intervention, and sprinkler system regarding the building and total dollar losses. Miscellaneous attribute, where suppression systems with different sizes are involved, is also found to register low fire damages, extensions, and dollar losses. 149 1 2 3 4 5 6 7 80246810CMs - FIsMFC1 2 3 4 5 6 70246810CMs - FIs A (25%~75%) B (25%~75%) C (25%~75%) D (25%~75%) E (25%~75%) F (25%~75%) G (25%~75%) Range within 1.5IQR Mean Line MeanDE1 2 3 4 5 6 70246810CMs - FIsFE1 2 3 4 50246810CMs - FIsBDL1 2 3 4 50246810CMs - FIsCDL1 2 3 4 50246810CMs - FIsTDL Note: CMs-FIs: Combustible materials that ignited in fire incidents, MFC: methods of fire control, DE: damage extension, FE: fire extension, BDL: building dollar loss, CDL: content dollar loss, TDL: total dollar loss. Figure 6-7: Notched box plot-based distributions among the CMs, methods of fire control and different impacts 150 Note: CMs-FIs: Combustible materials that ignited in fire incidents, MFC: methods of fire control, DE: damage extension, FE: fire extension, BDL: building dollar loss, CDL: content dollar loss, TDL: total dollar loss. The best methods in controlling fire extension to the room of origin were found to be portable extinguishing systems that cover handheld extinguishers, makeshift firefighting aids, and miscellaneous attributes. It was found that using a handheld extinguisher or makeshift firefighting aids could help 90% of the fires to be confined to the room of origin. Sprinkler systems and miscellaneous were found to be able to confine 91% and 92% of fire incidents to the room of origin, respectively. However, the rest mentioned methods of fire control were found to have less efficiency in confining the fire to the room of origin. The standpipe & hose systems-building were found to confine 54% of fire incidents, whereas fire department – water, fire department – other than water, and fixed system other than sprinklers confines 40%, 41%, and 68% of fire incidents, respectively. Figure 6-9 shows building dollar loss, content dollar loss, and total dollar loss to estimate the damage caused Figure 6-8: Contour Plot for response variables with two predictor variables 151 by the fire using different fire control methods. Similar to the fire extension, the top method of fire control that reduces total dollar loss was portable extinguishing systems. Total dollar loss using handheld extinguisher was found to be 76% VL, 19% L, 2% M, 3% H, and 0% VH. On the opposite, fire department – water is found to have 28% VL, 30% L, 13% M, 30% H, and 0% VH. Note: (1) Handheld extinguisher, (2) Standpipe & hose systems, (3) Makeshift firefighting aids, (4) Fire department – water, (5) Fire department – other than water, (6) Sprinkler system, (7) Fixed system other than sprinklers, and (9) Miscellaneous. BDL: building dollar loss, CDL: content dollar loss, TDL: total dollar loss The usage of extinguishers was found to be having low fire damages, extension, and dollar loss. Regarding fire extension with extinguisher as methods of fire control, 24% of the fire cases, confined to object of origin, 50% confined to part of room/area of origin, 16% confined to the room of origin, 4% confined to the floor level of origin, 5% confined to the building of origin, 1% extended beyond the building of origin. Concerning building dollar loss with extinguisher as methods of fire control, 54% of the cases have VL of building 0%10%20%30%40%50%60%70%80%90%100%BDL(1)CDL(1)TDL(1)BDL(2)CDL(2)TDL(2)BDL(3)CDL(3)TDL(3)BDL(4)CDL(4)TDL(4)BDL(5)CDL(5)TDL(5)BDL(6)CDL(6)TDL(6)BDL(7)CDL(7)TDL(7)BDL(9)CDL(9)TDL(9)Levels' PercentagesDollar loss (MFC)VL L M H VH Figure 6-9: Dollar loss with methods of fire control 152 dollar loss, 41% L, 1% M, 3% H, and 0% VH. Regarding content dollar loss, 83% of the cases are VL, 10% L, 3% M, 4% H, and 0% VH. Whereas for total dollar loss with an extinguisher, 76% of the cases are VL, 19% L, 2% M, 3% H, and 0% VH. Besides, the figures show correlated changes that could be useful to have a good ANN prediction model. 6.3.5. Prediction of fire impacts based on ANN The results of the fire impacts based on the LM-based ANN prediction model are determined, including MSE, R, and its performance (Figure 6-10). The training network of 4631 fire incidents with 1000 iterations stops generalization improvement at MSE of 1.36 and R of 0.65. The validation results with 992 fire incidents, the MSE is equal to 1.39, and R is 0.62. Whereas the testing results of 992 show that MSE is 1.34, and R is 0.65. The total results of training, validation, and testing show that MSE is 1.36, and R is 0.64. The error histogram with 20 bins shows that most of the datasets have an error lies with a value of 0.20. The MSE results of BR's training and testing are found to be equal to 1.36 and 1.38, respectively. Whereas the corresponding R values are equal to 0.64 and 0.63, respectively. The total results of training and testing show that MSE is 1.37, and R is 0.64. The MSE results of training, validation, and testing of SCG are equal to 1.40, 1.39, and 1.40, correspondingly. The related R values are equal to 0.63, 0.64, and 0.62, respectively. The total results of training, validation, and testing show that MSE is 1.4022, and R is 0.63. It can be seen from the R and MSE results that the Lavenberg-Marquardt algorithm has a preference in terms of prediction accuracy followed by the Bayesian Regularization and Scaled Conjugate Gradient algorithms, accordingly. Consequently, the Lavenberg-Marquardt algorithm is the most suitable to predict possible fire impacts based on the CMs-FIs and methods of fire control. The Lavenberg-Marquardt -based ANN model has a correlated ability of 65% in predicting the fire impacts of damage extension, fire extension, and dollar losses (building, content, and total dollar loss) using the CMs-FIs and methods 153 of fire control. An advanced script and MATLAB Matrix-Only Function are generated, as presented in the appendix, to produce results and solve similar problems. Additionally, the ANN Simulink diagram is generated, as presented in Figure 6-11, to simulate ANN in Simulink or deploy with Simulink Coder tools. The Simulink diagram is an efficient visual technique to predict the possible fire impacts, including damage extension, fire extension, and dollar losses (building, content, and total dollar loss) based on the CMs-FIs and the methods of fire control. Figure 6-10: Results of Artificial Neural Network using the LM-based algorithm 154 Note: CMs-FIs: Combustible Materials that ignited in Fire Incidents, MFC: Methods of Fire Control, DE: Damage Extension, FE: Fire Extension, BDL: Building Dollar Loss, CDL: Content Dollar Loss, TDL: Total Dollar Loss. With respect to the first research question, regarding the potential CMs-FIs in SG-MURBs, it was found that SG-MURBs have 36 potential combustible materials that are classified under ten main attributes. At the same time, 50 green features were found to have a combustibility potential from different perspectives, including ignitable, heat release, growth rate, flame spread, and suppression effectiveness. The green features were categorized mainly under building components and forestry product categories. Smart features were found to be categorized under electrical equipment. Different building codes in BC, including the National Building Code of Canada, the BC Building Code, and Vancouver’s Building Bylaw, were requiring protection measures such as exit systems and fire sprinklers [451]. However, the investigation of this study is comprehensive and the first of its kind to help define specific fire safety strategies, especially fire suppression materials and methods of fire control, for each CM-FIs in SG-MURBs as it is encouraged by the international fire code [292]. Figure 6-11: Artificial neural network-based Simulink diagram 155 The second question in this study sought to determine the most critical CMs-FIs in SG-MURBs. It can be seen from the results that the most critical CMs-FIs were determined quantitatively using the developed benchmarks. In BC, almost 30% of the CMs-FIs were critical under H and VH risk levels. The defined critical factors help the decision-makers prioritize and take immediate actions to define the CMs-FIs that should be avoided, minimized, or transferred to other parties, such as insurance companies. This supports the GRID Architects, as some commonly used materials are now non-compliant [452]. Furthermore, this benchmarking helps also to install and invest in the appropriate fire protection safeguards. The assessed highly critical combustible materials in SG-MURBs help find the best preventive and protective strategies, particularly after integrating smart and green features in buildings. Furthermore, although smart features could cause fires [433], particularly with the control-room, installing the features appropriately, taking prevention and protection strategies to reduce fire relative frequencies, fire extension, fire damages, and related-dollar loss. Especially as more than 70% of the fires in SG-MURBs of the seven cities were found to be related to equipment and appliances. This supports the claim of CURRAN [434], where smart devices help to double-check the equipment easily; besides, they help to get alerts as reminders and for dangerous-cases aiding to enhance fire prevention and protection. Almost half of the green features have a high combustibility level. Whereas most of the insulations are foam-based products using petroleum derivatives, such as polyurethane and expanded polystyrene; they can accelerate the destruction of the entire building. Insulation products and vegetative roofs increase fire risk and could endanger life and property. In addition, the amount of toxic smoke that could be produced by these insulations could present a major pollution hazard to the environment. This may also make it almost impossible for firefighters to intervene. A vegetative roof system needs a careful evaluation 156 when choosing plants, as volatile oil “essential oil” based plants, including tea, clove, lavender, and fragrance that could help fires spread. Also, dried plants are highly combustible materials for a fire. Therefore, sustainability assessment tools, building codes, and design standards should incorporate fire hazard criteria to improve fire prevention, protection, and intervention strategies. Preventive strategies for CMs-FIs could involve (i) designing, including usage of non-combustible materials. (ii) Appropriate techniques should be followed to store and use such products, including flammable liquids and flammable gases. (iii) Training and education to increase community awareness about fire prevention techniques and the critical combustible materials in SG-MURBs. Consequently, smart features could enhance fire prevention and protection strategies; however, green features could jeopardize and speed up fire conditions. Furthermore, using the developed approach helps determine the most appropriate fire suppression materials and methods of fire control for SG-MURBs quantitatively instead. Concerning the third question, the appropriate fire suppression materials and methods of fire control were determined. Interestingly, focusing on dollar loss with a very high level, it was found that using extinguishers for fire suppression in SG-MURBs help to reduce dollar loss to 10 times compared with fire department intervention, almost 5 times compared with standpipe & hose systems-building, and 4 times compared with sprinkler systems. Furthermore, the usage of all the portable extinguishing systems was found to have a slight dollar loss compared with the other fire protection systems, including sprinklers, standpipe, and fire department intervention. The effectiveness of portable extinguishing systems could be due to different reasons, including ease to handle, ability to attack fire origin, and low response time. This helps to enhance the protection of people, properties (building and contents), and the environment. Besides, it helps to improve the related regulations and codes. According to the national fire code-Canada, “portable 157 extinguishers shall be located in or adjacent to corridors or aisles that provide access to exits.” However, installing extinguishers at SG-MURBs should be required as their usage records the low level of fire damages, extensions, and dollar losses compared with other methods of fire control. This supports the claim of the National Fire Protection Association (NFPA 10 - Table 13.6.1.2), where fire extinguishers should be provided for fire protection regardless of the presence of any fixed fire suppression systems. Regarding the fourth research question, the ANN model is developed to predict five response variables, including damage of fire, fire extension, dollar loss (BDL, CDL, and TDL), using the predictors CMs-FIs and methods of fire control. According to the rule of thumb of a correlation, the developed ANN model is strong. The ANN model helps to predict fire impacts, including damages and extension of fire and dollar loss, just by knowing the CMs-FIs and the available methods of fire control. Furthermore, the Simulink model is developed to implement the ANN model as a stress-free technique to perform and predict all the possible scenarios. 6.4. Summary This study developed an integrated approach for fire risk assessment and protection based on combustible materials for smart-green multi-unit residential buildings. This approach assesses the fire risks and then identifies appropriate fire suppression materials and methods of fire control. The results found that thirty-six CMs-FIs to be the potential factors in SG- MURBs. The developed framework was applied to seven cities of British Columbia, Canada. The most critical combustible material factors with the very high-risk level for each city are determined. Kamloops, Vancouver, Surrey, and Kelowna found a high percentage of combustible material factors with very high-risk levels. In BC, the critical combustible material factors under high and very high-risk levels are determined, whereas 5% of combustible material factors are found to be high, and 24% are very high. Regarding 158 green features with maximum values, 45% of the features have a high combustibility level. The most appropriate fire suppression materials for the top 10 critical combustible material factors are identified, water is appropriate for 70% of combustible material factors, foam (80%), dry-powder (100%), CO2 (20%), and wet-chemical (70%). It is noteworthy that fire damage levels are extended beyond the building of origin, are recorded with flammable gas fires where standpipe and hose systems are used. Remarkably, the usage of extinguishers is found to have low fire damages and dollar loss. Finally, the developed ANN model using the Lavenberg-Marquardt algorithm is strongly able to predict five response variables, including damage extension, fire extension, and dollar losses, using combustible material factors and methods of fire control. Furthermore, the developed Simulink model helps to perform and predict all the possible scenarios. Consequently, it is recommended for decision-makers to review fire policies and regulations, such as enforcing the usage of appropriate methods of fire control and fire suppression materials for the combustible material factors in SG-MURBs for effective and efficient fire protection. 159 CHAPTER 7: A DATA-DRIVEN MODELS FOR FIRE SAFETY ASSESSMENT 7.1. Background Fire catastrophic consequences emphasize the need to develop and use different safety strategies for fire protection and intervention, including initial fire detection, action taken, method of fire control and extinguishment, and transmission of alarm to the fire department [32]. Fire initial detection covers different technical factors, including smoke and heat alarms, smoke and heat detectors, and automatic sprinkler system [91][129][453]. Action taken covers the extinguishment process by an occupant, fire department, or by the automatic system; whereas the method of fire control involves all the possible systems that could extinguish fires, such as handheld extinguisher, standpipe and hose systems, makeshift firefighting aids, sprinkler system, and fire department [90][91][223][261]. Transmission of alarm to fire department takes into account the potential ways for informing fire department, including telegraph or telephone systems, radio systems, private fire alarm system, radio, direct verbal report to a fire station, telephone tie-line to fire department, siren, horn, or another alerting device. The availability of a variety of fire strategies highlights the need to determine the optimal combination to control fire risk impacts effectively [31][90][294][327][444]. A framework for fire safety assessment helps identify the potential safety strategies, analyze and evaluate the related impacts, and select the best strategies, by taking into consideration different impacts, including fire extension, fire damages, building dollar loss, content dollar loss, and total dollar loss [57][209][411][445][446]. Furthermore, to the best of the authors’ knowledge, investigating fire safety strategies quantitatively in SG-MURBs is lacking in the peer-reviewed literature. An assessment approach for safety strategies is required to investigate, predict, and optimize fire protection and intervention techniques to control the related potential impacts. Moreover, limited 160 approaches were found to predict fire-related impacts in SG-MURBs, which could help to prepare and improve fire protection and intervention strategies accordingly. This study aims to develop an integrated approach to investigate and assess fire safety strategies; the approach attempts to address the following critical questions to enhance fire safety strategies in SG-MURBs using ANN: (v) What are the key potential factors of fire protection and intervention strategies for SG-MURBs? (vi) What are the most effective fire safety strategies for very low fire impacts? (vii) How to predict fire impacts using fire protection and intervention strategies? (viii) What is the optimal combination of predictors that minimize fire impacts? 7.2. Methodology This study proposed an approach (Figure 7-1) to address the above-mentioned questions. The approach is based on four main steps: (i) investigate possible fire strategies in SG-MURBs; (ii) assess fire strategies based on their association with the related fire extension, damages, and dollar loss; (iii) develop an ANN model to predict fire impacts based on the available protection and intervention strategies. Moreover, use Simulink to perform analysis of all possible scenarios; and (iv) optimize the combination set of predictors that minimize fire impacts. The developed approach is applied to British Columbia using real-world data of fire incidents in SG-MURBs. In the context of this study, safety strategies refer to the fire protection and intervention techniques that are used in SG-MURBs to detect and extinguish fires. Safety strategies ensure that all aspects of fire safety, including protection and intervention, are fully integrated into SG-MURBs for effective fire risk management. Details of the developed framework are presented in the following sub-sections. 161 CATEGORIZATION AND IDENTIFICATIONPREDICTION OPTIMIZATIONINITIAL DETECTION (ID)ACTION TAKEN (AT)METHOD OF FIRE CONTROL AND EXTINGUISHMENT GROUP (MFCE-G)Optimal solutionOptimization plotDefines the scope and gap for the fire risk managementDefines the possible fire safety strategies Assess all the strategiesIdentify the critical fire safety strategiesDevelop ANN modelPredict the possible fire impacts Determine the optimum set of safety strategiesImprove protection and intervention strategiesNFIDPERFORMANCE OF SMOKE ALARM DEVICE (PSAD)TRANSMISSION OF ALARM TO FIRE DEPARTMENT (TAFD)Fire extensionFire damagesBuilding dollar lossContent dollar lossTotal dollar lossOccupant response and evacuationPerformanceTraining stateError histogramRegressionSimulation ANN-based modellingFactors selectionMultiple responses optimizationFire safety regulationsUpdatesPREDICTORS RESPONSESINVESTIGATIONASSESSMENTASSOCIATIONCORRELATIONAnalysis among the predictors and the related responses 7.2.1. Investigation of protection and intervention strategies National fire information database (NFID) is used in this study to investigate the current fire strategies in SG-MURBs to identify the potential safety factors for protection and intervention; where these factors are categorized into five predictors (Table 7-1). This investigation helps to enhance fire protection and intervention strategies accordingly. The categorization of the predictors covers initial detection (ID), action taken (AT), method of fire control and extinguishment group (MFCE-G), the performance of smoke alarm device (PSAD), and transmission of alarm to fire department (TAFD) to cover all possible safety strategies for SG-MURBs. ID refers to the predictor that allowed fire incidents to be detected first. AT refers to the action taken to extinguish the fire using specific MFCE-G, and TAFD describes the techniques in which the fire department was first notified. Figure 7-1: Integrated approach to investigate, predict, and optimize fire safety strategies 162 Predictors INITIAL DETECTION (ID) Code PERFORMANCE OF SMOKE ALARM DEVICE (PSAD) Code TRANSMISSION OF ALARM TO FIRE DEPARTMENT (TAFD) Code Smoke alarm device 1 Alarm in room of origin - activated 1 Coded signal municipal fire alarm system (included are telegraph or telephone systems, radio systems, and auxiliary connection to them) 1 Smoke detector device 2 Alarm not in room of origin - activated 2 Private fire alarm system (included are signals received from central stations and remote stations) 2 Heat alarm device 3 Alarm in room of origin – not activated – non-suitable location 3 Radio 4 Heat detector device 4 Alarm in room of origin – not activated – battery dead 4 Direct verbal report to a fire station 5 Automatic sprinkler system 5 Alarm in room of origin – not activated – no battery 5 No alarm received (includes alarms when delayed appreciably resulting in late or no response) 6 Automatic system other than sprinkler 6 Alarm in room of origin – not activated – AC not connected/disabled 6 Telephone tie-line to fire department - the reporting person direct dials another emergency agency (included are all calls received over a direct telephone tie-line from another emergency call centre or facility tied directly to the fire department, calls forwarded to fire department by outside 911 dispatch facility or central alarm and monitoring facility) 7 Visual sighting or other means of personal detection 7 Alarm in room of origin – not activated – mechanical failure 7 No initial detection (burned out before detection) 8 Alarm not in room of origin – not activated – battery dead 8 Initial detection – unclassified 9 Alarm not in room of origin – not activated – no battery 9 Specialty detector (includes flame, beam line) 11 Alarm not in room of origin – not activated – AC not connected/disabled 10 ACTION TAKEN (AT) Code Alarm not in room of origin – not activated – mechanical failure 11 Extinguished by occupant 1 Not enough smoke to activate smoke alarm 12 Siren, horn, or other alerting device 8 Burned out - no extinguishment attempted 2 Alarm location unknown – activated 50 Transmission of alarm to fire department - unclassified 9 Extinguished by fire department 3 Alarm location unknown – not activated - non-suitable location 51 911 10 Extinguished by automatic system 4 Alarm location unknown – not activated – no battery or battery dead 52 Not applicable (e.g. person) 88 Minor fire - no action taken (i.e. exposure damage, blistering) 5 Alarm location unknown – not activated – AC not connected/disabled 53 Transmission of Alarm to Fire Department - unknown 0 Action taken – unclassified 9 Alarm location unknown – not activated – mechanical failure 54 METHOD OF FIRE CONTROL AND EXTINGUISHMENT GROUP (MFCE-G) Code Alarm location unknown – not activated – reason unknown 55 Handheld extinguisher 1 Alarm in room of origin – not activated – other reason 70 Standpipe & hose systems (building) 2 Alarm in room of origin – not activated – reason unknown 71 Makeshift firefighting aids 3 Alarm not in room of origin – not activated – other reason 80 Fire department - water 4 Not applicable 88 Fire department – other than water 5 Smoke alarm activation - unknown 99 Sprinkler system 6 No smoke alarm 0 Fixed system other than sprinklers 7 Burned out 8 Miscellaneous 9 Table 7-1: Details of the five predictors with their factors for fire impacts 163 The fire impact responses are categorized into six main attributes, as presented in Table 7-2, including fire extension (FE), fire damages (FD), building dollar loss (BDL), content dollar loss (CDL), and total dollar loss (TDL) based on the predictor variables of protection and intervention. Fire extension shows the actual extent of burning, excluding browning or blistering of paint. Damage extension refers to the total extent of fire damages, including damages caused by heat, smoke, and water as an extinguishing agent. BDL, CDL, and TDL estimate the building damage caused by fire, the loss of the contents of the property in dollars, and the total loss includes BDL and CDL, respectively. A count chart is used to show comparisons among the safety factors leading to identify percentage for a specific factor to help for the assessment. Finally, protection and intervention strategies are assessed to determine the most effective strategies for very low fire impacts. This would help install, invest, and enforce the appropriate strategies to protect people, properties, and the environment accordingly. Association analysis is used as an interesting technique to discover hidden relationships among the predictors and the responses. The uncovered relationships can be represented in the form of association rules or sets of frequent items. Correlation among the predictors and the responses is analyzed to reveal the possibility of significant relationships that would help for an effective prediction. 7.2.2. ANN-based prediction An ANN model was developed to help enhance fire protection and intervention strategies accordingly. Five predictors (Table 7-1) were used to predict the possible fire impacts accordingly. The correlation between the predictors and the responses was analyzed. Regarding the responses, occupant response/ evacuation (ORE), fire extension (FE), and fire damages (FD) are categorized into different categories to present fire impacts. Five levels are defined for BDL, CDL, and TDL based on the recorded dollar loss from the previous fire incidents. The levels include very high (VH=5), high (H=4), medium (M=3), 164 low (L=2), and very low (VL=1). Detailed attributes of the predictors and the responses are presented in Tables 7.1-7.2. The ANN model was developed using three algorithms to determine the best algorithm with prediction accuracy, including Lavenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) backpropagation algorithms [431]. Table 7-2: Details of the ire impacts responses Fire damages (FD) Fire extension (FE) Dollar Loss 1 Confined to part of room/area of origin 1 Confined to object of origin Levels BDL CDL TDL 2 Confined to a room of origin 2 Confined to part of room/area of origin VL $ 5,000.00 $ 10,000.00 $ 20,000.00 3 Confined to the floor level of origin 3 Confined to room of origin L $ 50,049.94 $ 22,625.84 $ 75,175.78 4 Confined to the building of origin 4 Confined to floor level of origin M $ 95,099.87 $ 35,251.69 $ 130,351.56 5 Extended beyond the building of origin 5 Confined to building of origin H $ 20,047,549.94 $ 5,017,625.84 $ 25,065,175.78 6 Confined to roof 6 Extended beyond building of origin VH $ 40,000,000.00 $ 10,000,000.00 $ 50,000,000.00 7 Not applicable (includes vehicle, outside area) 7 Confined to roof Dollar loss levels, very high (VH), high (H), medium (M), low (L), and very low (VL) are determined based on recorded values of fire incidents for max value (max) of dollar loss for fire incidents, average (max, average), average, average (average, min), and average (average (average, min), min), respectively. Occupant response/ evacuation (ORE) CODE Occupants evacuated safely 1 Occupants did not evacuate – alarm inaudible 2 Occupants did not evacuate – physically/mentally challenged 3 Occupants did not evacuate – age related (infants/aged) 4 Occupants did not evacuate – unnecessary to evacuate 5 Occupants did not evacuate – suspected influence of drugs/alcohol 6 Occupants did not evacuate – unable to respond (reason unknown) 7 Some (not all) occupants evacuated safely 8 Occupant response/evacuation - unknown 99 Not applicable/no occupants/alarm did not activate 0 7.2.3. Optimization analysis 7.2.3.1. A factorial-based response optimization A factorial-based response optimization is used to help identify the optimum combination of predictors that optimize a set of responses. This would help to evaluate the impact of multiple variables on the responses. Minitab (Minitab® 19.1) is employed to determine the optimal responses based on the defined requirements for each response, including minimizing the response of fire extension (FE), fire damages (FD), building dollar loss (BDL), content dollar loss (CDL), and total dollar loss (TDL), the lower impacts the better; whereas ORE is considered to be (1) as a target to have occupants evacuated safely. 165 Furthermore, an optimization plot is drawn to show the main effects among the predictors and the responses. 7.2.3.2. Optimization using ANN and multiobjective-based genetic algorithms A model was developed integrating artificial neural networks with genetic algorithms to select critical potential factors as the optimal set of fire safety strategies that should be considered to enhance fire protection and intervention strategies. The ANN model was developed based on the function fitting neural network (i.e. fitnet) with 1 hidden layer with 50 neurons. The ANN model was trained using the Levenberg-Marquardt optimization backpropagation algorithm (i.e. 'trainlm'). Other training algorithm can be 'traingd', 'trainbr', 'traingdm', 'traingdx', and 'traincgp', of which we obtained the best performance with the 'trainbr'. The estimation performance of the ANN model was calculated using the perform function. INITIAL DETECTION ACTION TAKEN METHOD OF FIRE CONTROL AND EXTINGUISHMENT GROUPFIXED SYSTEM OTHER THAN SPRINKLERS Sprinkler Protection Training state Error histogramPerformanceRegression Simulation PREDICTORSTRANSMISSION OF ALARM TO FIRE DEPARTMENT Manual Fire Protection Facilities Fire extensionFire damagesBuilding dollar lossContent dollar lossRESPONSESDefine the possible fire safety strategies Prepare, invest, And improve the strategies accordinglyDevelop artificial neural network modelSelection Crossover Mutation Obtain the optimum combimnationGA-based optimizationSelect the best fire safety strategies to reduce fire impactsW1WmW2 bSumWeightsQ(.)Activation functionOutputYDetermine predictors Predict responses Figure 7-2: Process for fire strategies assessment in SG-MURBs 166 The code uses the default training parameters of the 'trainbr' training function, which are shown in Table 7-3. Different training methods (including trainlm, traingdx, traincgp, trainbr) and the number of layers (1 - 3 layers) were applied, and the 1-layer ANN with the trainbr training function was selected as it showed the best performance. The One Hot Key Encoder was not used as it did not improve the performance of the ANN. The developed MATLAB code for ANN modelling after importing the data, including the model Inputs and Targets is presented as follows: %import Inputs with size: number of samples * number of variables %import Targets with size: number of samples * number of output variables global net %net will be used for optimization via GA net = fitnet([50],'trainbr'); net = train(net,Inputs,Targets); output = net(Inputs); perf_train = perform(net,output,Targets); Training Parameters net.trainParam Value Maximum number of epochs .epochs 1000 mu: Initial /Maximum .mu / .mu_max 0.001 / 1e10 mu increase / decrease factor .mu_inc / .mu_dec 10 / 0.1 Performance goal .goal 0 Minimum performance gradient .min_grad 1e-7 Maximum number of validation failures .max_fail 6 The multiobjective-based genetic algorithm is an exceptionally effective technique in providing a search strategy, generating possible solutions, and optimizing multidimensional problems [457][458]. The genetic algorithm is considered to have three fundamentals: (i) an ability to define fitness criterion; (ii) ability in articulating the solution as a chromosome, generally as an array of bits; and finally (iii) ability in imitating sexual reproduction, crossover and mutation; crossover is a process of exchanging bits between the solutions. The crossover of two-parent strings produces new solutions, whereas mutation is generating new solutions by flipping some bits of a string. An objective function (Obj) in a genetic algorithm, as presented in Eq(7-1), describes the objective of the optimization [459]. Table 7-3: Training parameters of the 'trainbr' training function 167 𝑂𝑏𝑗 =∑ (𝑑𝑖(𝑝𝑖) ∙ 𝑤𝑒𝑖𝑔ℎ𝑡𝑖)𝑃𝑖=1∑ 𝑤𝑒𝑖𝑔ℎ𝑡𝑖𝑃𝑖=1 𝐸𝑞(7 − 1) where the formula is normalized, by dividing with the sum of all the weights; 𝑤𝑒𝑖𝑔ℎ𝑡𝑖 correspond to the relative importance of each property 𝑖; 𝑑𝑖 represents the desirability as a function that should be low in this case as minimizing the outcome of risk. The developed code for multiobjective-based GA optimization is presented as follows: %X = ga(FITNESSFCN,NVARS,A,b,Aeq,beq,lb,ub,NONLCON) ObjectiveFunction= @(x)[obj1(x) obj2(x) obj3(x)]; nvars = 7; % Number of variables A1=[]; b1=[]; Aeq1=[]; beq1=[]; LB1 = [1 1 1 1 1 1 1]; % Lower bound UB1 = [4 4 4 7 5 6 5]; % Upper bound ConstraintFunction=[]; opts = optimoptions(@gamultiobj,'MaxGenerations',300,'MaxStallGenerations', 100); %EVALUATION [bestX,bestfval]=gamultiobj(ObjectiveFunction,nvars,A1,b1,Aeq1,beq1,LB1,UB1,ConstraintFunction,opts) where obj1(x) obj2(x) obj3(x) are separate functions defined as function y=obj1(x) global net y1=net(x'); y=round(y1(1,:)); end function y=obj2(x) global net y1=net(x'); y=round(y1(2,:)); end function y=obj3(x) global net y1=net(x'); y=round(y1(3,:)); end 7.3. Results and Discussions This section presents the results and discussions of protection and intervention strategies in SG-MURBs, interpreting the significance of the findings. 7.3.1. Investigating the potential fire protection and intervention strategies with possible impacts 7.3.1.1. Investigating the predictors: fire protection and intervention strategies Fire incidents in BC were investigated, taking protection and intervention strategies into consideration that expects to develop effective and efficient fire safety management. ‘Effective’ refers to the usage of appropriate extinguishing systems, while ‘efficient’ is an extinguishment in a short time with low impacts. The predictors were investigated, including different factors, as presented in Table 7-1. The top three factors for ID strategy 168 to detect fires were found to be smoke detector device with 7%, smoke alarm device with 16%, and 58% of the fires were detected with a visual sighting or other means of personal detection. Concerning the performance of smoke alarm device (PSAD), the smoke alarm device was found to be activated for 67% of the occurred fires, 2% of the alarms did not activate, and no smoke alarm was found for 8% of the occurred fires. Regarding the transmission of alarm to the fire department (TAFD), as Figure 7-3 presents the first 100 incidents, 86% of the fires were transmitted to the fire department through telephone tie-line to the fire department. The reporting transmission alarm covers the calls over a direct telephone tie-line from another emergency call centre, facility tied directly to the fire department, calls forwarded to the fire department by outside 911 dispatch facility, and central alarm and monitoring facility. It was found that 3% of the fires were transmitted through municipal fire alarm systems, including telephone and radio systems. In comparison, 2% of the fires were transmitted directly and verbally to a fire station and through a private fire alarm system. Regarding the action taken, 54% of the fires were found to be extinguished by the fire department, 28% of the fires were extinguished by occupants, and just 6% of the fires were extinguished by the automatic system. Finally, for the method of fire control and extinguishment group (MFCE-G), it was found that 47% of the fires were extinguished by the fire department using water. Whereas 17% of the fires were handheld extinguished, 4% extinguished with sprinkler systems, and 3% of the fires extinguished with standpipe and hose systems of the building. 169 Note: TAFD: Transmission of Alarm to Fire Department, and AT: Action Taken. 7.3.1.2. Investigating the responses: fire impacts As responses, the impacts of fire incidents were investigated from different perspectives in order to employ the best strategy for fire safety. The responses cover occupant response/ evacuation (ORE), fire extension (FE), fire damage (FD), building dollar loss (BDL), content dollar loss (CDL), and total dollar loss (TDL). Figure 7-4 presents 100 incidents of two responses, whereas the details of the responses are presented in Table 7-2. Regarding the ORE, occupants evacuated safely from 38% of the occurred fire incidents. In contrast, occupants did not evacuate, as it was unnecessary to evacuate, for 15% of the fires. At the same time, occupants did not evacuate and were unable to respond to 4% of the fire incidents. Besides, the response and evacuation of the occupants were found to be unknown for 42% of the occurred fire incidents. Concerning the FE, 29% of the fires were found to 012345678910TAFD012341 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97ATFIRE INCIDENTSFigure 7-3: Transmission of alarm and the taken actions for the first 100 investigated fire incidents 170 be confined to part of the room or area of origin; 26% of the fires were confined to object of origin; 16% confined to the building of origin. Besides, 13% of the fires were found to be confined to the room of origin; 7% of the fires were confined to the floor level of origin, and 8% of the occurred fires extended beyond the building of origin. In case of fire damages, 30% of the fires extended the damages beyond the building of origin; 22% of the fires had damages confined to a room of origin; 18% had damages related to a room of origin; the same percentage (11%) of the fires were found to be related to floor level and building of origin. Whereas 6% of the fires were found to have damages confined to the roof. Note: FD: Fire Damages, and TDL: Total Dollar Loss. Regarding the BDL, 45% of the fires recorded very low building dollar loss, 26% reached very high dollar loss, 14% had high dollar loss and 8% for both low and medium dollar loss. Concerning the CDL, 63% of the fires had very low content dollar loss, 20% high, 1234567FD0123451 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97TDLFIRE INCIDENTS Figure 7-4: Fire damages and total dollar loss of the first 100 investigated fire incidents 171 10% medium, and 8% low content dollar loss. Finally, for the TDL, 64% of the fires had very low total dollar loss, 18% had high dollar loss, 10% reached medium dollar loss, and 8% of the fires had found to have low total dollar loss. 7.3.2. Association and correlation analyses among predictors with responses An investigation of the association among different predictors and responses of 2878 fire incidents can help discover the most effective strategies to reduce fire impacts in SG-MURBs. Figure 7-5 shows, in the upper part, the association of the initial detection with total dollar loss. It can be seen that smoke alarm devices and visual sighting are the two main fire detection factors. However, it was found that 58%, as the highest percentage, of the fires were detected by the visual sighting, whereas just 16% of the fires were detected by smoke alarm devices. Furthermore, the type of detection technique is varying significantly with levels of fire impacts. It was found that 73% of the fires with visual sighting is associated with very low total dollar loss, whereas 78% of fires with smoke alarm were found to record very low total dollar loss. Moreover, the visual sighting was found to be associated with L (7%), M (7%), H (12%), and VH (0.2%). In contrast, the smoke detector was associated with L (7%), M (6%), H (7%), and VH (0.2%). Regarding the transmission of alarm to the fire department (TAFD), it was found that 82% of the fires had a transmission of alarm to the fire department using a telephone tie-line. Among which, 74% had VL of TDL, 7% L, 7% M, 11% H, and 0.2% VH. It can be noticed that the rates of fire impact levels with TAFD are almost similar with both detection strategies. 172 Note: ID: Initial Detection, TAFD: Transmission of Alarm to Fire Department, and TDL: Total Dollar Loss. As an essential strategy for fire detection, smoke alarm device was investigated from a different perspective to enhance its performance. It was found that the alarm was activated for 67% of the investigated fire incidents. Whereas 2% of alarms were not activated, 8% of the fires had no smoke alarm, and the activations were unknown for 23% of the incidents. Besides, the association of smoke alarm device performance with fire damages was investigated, as Figure 7-5 shows. The figure shows that most of the fire incidents are related to a known and an unknown location of a fire alarm. It was found that 3% of fires with an alarm that is activated in a room of origin, as known location, was associated with total dollar loss; in which 18% of these fires were associated with medium total dollar loss (TDL) and 30% were associated with high TDL. In contrast, 50% of the fires had an alarm activated with a location unknown. Among which, it was found that 78% of the fires had 3593429 321180119 141 7 2 2 333 8 37013 17 238 11234118113 2055 9 3 3 2 31/11/21/31/41/52/12/22/32/42/53/13/23/44/14/24/34/45/15/25/35/46/16/37/17/27/37/47/58/19/19/29/39/4COUNTID/TDL633 4 6 242 3897 16 171 4 1 112 2 131749178164 2596 271 3 41/11/21/31/42/12/22/43/13/23/33/43/54/14/45/15/25/46/17/17/27/37/47/59/19/29/39/4COUNTTAFD/TDL Figure 7-5: Association of the ID and TAFD with the total dollar loss 173 damages confined between a part of a room and the building of origin, and 20% of these fires had damages extended beyond the building of origin. Furthermore, 76% of these fires had VL of TDL, 7%, 6%, and 10% L, M, and H of TDL. Note: PSAD: Performance of Smoke Alarm Device, FD: Fire Damages, and TDL: Total Dollar Loss. The actions that were taken (AT) to extinguish fires were investigated as an essential response to the initial detection to control fire impacts, as presented in Figure 7-7. It was found that 28% of the fires were extinguished by occupants, 54% were extinguished by the fire department, and just 6% extinguished by the automatic system. The association between the actions that were taken and the total dollar loss was investigated. It can be seen from the presented incidents in the figure that the actions were taken mainly by a fire department, occupant, and automatic suppression, respectively. It was found that 48% of the action was by a fire department; among which 12% of the actions had medium total 1 10 100 1000 10000Alarm in room of origin - activatedAlarm in room of origin – not activated – battery deadAlarm in room of origin – not activated – no batteryNot enough smoke to activate smoke alarmAlarm location unknown – activatedAlarm in room of origin – not activated – other reasonSmoke alarm activation - unknownNo smoke alarmCountPSAD2712 14 235 1 2 2 4 3 6 2111410486 1485 4 2 8 5 11/11/21/31/412/112/24/24/45/15/25/35/450/150/250/350/450/570/170/270/370/488/1COUNTPSAD/TDL31841622317928924650/150/250/350/450/550/650/7COUNTPSAD/FDFigure 7-6: Identification and association of the PSAD with total dollar loss and fire damages 174 dollar loss (TDL), and 18% had high total dollar loss. Besides, 25% of the actions were taken by the occupants, in which just 0.2% of the actions had medium TDL, 1.2% had high TDL, and 0.1% of the actions had very high TDL. Automatic suppression was found to be taking action for 6% of the fires; whereas 11% of the incidents had medium TDL, and 17% had high TDL. Furthermore, statistical test results with Spearman correlation found that p-value < 0.05 and R > 0.5; therefore, the correlation among the predictors and the responses was statistically significant. Note: AT: Action Taken, and TDL: Total Dollar Loss. A contour plot is shown in Figure 7-8 to investigate the relationship between the content dollar loss and two predictors. This shows that having a sprinkler system with a smoke detector device leads to having a high level of content dollar loss. Whereas having a sprinkler system or fire department intervention with almost all initial detection techniques caused a medium level of content dollar loss. This also shows that having partial sprinkler protection with supervised or watchman service and fire department intervention caused a 0 500 1000 1500Extinguished by occupantBurned out - no extinguishment attemptedExtinguished by fire departmentExtinguished by automatic systemMinor fire - no action taken (i.e. exposure damage, blistering)Action taken – unclassifiedCOUNTAT693172 9 12094 1 4829149163 25361031719 29 493 202 2 21/11/21/31/41/52/12/22/32/43/13/23/33/43/54/14/24/34/45/15/29/19/29/39/4COUNTAT/TDL Figure 7-7: Identification and association of the action taken with the total dollar loss 175 content dollar loss to reach the high and very high levels. It can also be seen that in case of having a partial sprinkler system or not having it at all with the intervention of the fire department gives a wide range of having a medium level of content dollar loss. Regarding the fire safety strategies of the initial detection and transmission of alarm to the fire department, it can be seen that having a smoke detector device or automatic sprinkler system with a private fire alarm system or radio, respectively, caused a very high level of content dollar loss. Based on these strategies, it can be seen that there was a higher potential for a high level of content dollar loss. Moreover, having a fire department or sprinkler system as the possible taken actions with a private fire alarm system or radio have a range of high and very high levels of content dollar loss. Note: CDL: Content Dollar Loss, ID: Initial Detection, MFCE: Method of Fire Control and Extinguishment, SP: Sprinkler Protection, AT: Action Taken, TAFD: Transmission of Alarm to Fire Department. Figure 7-8: A contour plot for content dollar loss and several predictors 176 7.3.3. Prediction analysis An ANN model was developed based on different predictors, including initial detection and the taken actions to predict potential fire impacts, to enhance fire protection and intervention strategies accordingly. The ANN-based prediction results were found to be robust for all the algorithms, including LM, BR, and SCG. Taking into consideration the R values of training, validation, testing, and mean, Figure 7-9 shows that the BR algorithm has a preference to predict fire impact accuracy, followed by LM and SCG algorithms accordingly. Prediction analysis was done with different neurons to select the optimum ones without overfitting in the developed ANN model. Note: LM: Lavenberg-Marquardt, SCG: Scaled Conjugate Gradient, and BR: Bayesian Regularization backpropagation algorithms. Figure 7-9: R-values of ANN model with LM, BR, and SCG algorithms 0.9200.9420.9280.9390.9290.9310.9350.9370.9360.9440.9160.8090.8660.6240.9040.8200.8820.7850.8530.7950.9180.8750.8970.7820.9170.8750.9080.8610.8950.8691 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0R-VALUES : BRTraining Testing Mean0.9160.9360.9270.9340.9440.9300.9030.9340.9230.9340.8680.8370.8130.760 0.9190.8400.8760.8490.612 0.8480.9010.738 0.8820.8670.6850.8010.7150.6890.7730.7630.8950.8370.8740.8540.8490.8570.8310.8240.7690.8481 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0R-VALUES : LMTraining Validation Testing Mean0.8950.9080.9100.8960.8590.9180.715 0.8960.9100.9080.9160.9060.8780.8520.8140.9350.7490.766 0.9470.8940.9130.8320.9500.9090.8850.8340.691 0.8450.7880.8820.9080.8820.9130.8860.8530.8960.7180.8360.8820.8951 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0R-VALUES : SCGNEURONS Training Validation Testing Mean 177 It was found that the optimum R-value for the BR algorithm was associated with the 10th neurons reaching an R-value of 0.92 at epoch 298 for training, testing, and mean. Whereas the optimum R values, as presented in the figure, for LM and SCG are found to be at 10 and 30 neurons, respectively. Figure 7-10 shows the training results with 1000 iterations for the BR algorithm that was found to stop generalization improvement at R of 0.927. The testing results show that R was 0.921, whereas the total R-value was 0.926. The error histogram with 20 bins shows that most of the datasets have a low error. The BR algorithm-based ANN model was found to have an ability with R2 of 85 % in predicting possible fire impacts based on the investigated predictors. Figure 7-10: Results of the BR-based ANN for prediction of fire impacts 178 An advanced script and MATLAB function, as shown in the appendix, were generated to produce results and solve similar problems. Additionally, Figure 7-11 presents the generated Simulink diagram to simulate ANN results. The Simulink diagram was found to be an efficient technique to visualize the possible scenarios of the responses. Furthermore, the Simulink discovers two main patterns for fire impacts; the first is related to fire extension and fire damage, the high fire extension the serious damages could be expected accordingly. Whereas the second pattern is concerned with CDL and TDL, the high content of dollar loss, the high total dollar loss is anticipated. This could help in controlling fire impacts based on priorities and correlated responses. Note: ID: Initial Detection, PSAD: Performance of Smoke Alarm Device, TAFD: Transmission of Alarm to Fire Department, AT: Action Taken, MFCE: Method of Fire Control and Extinguishment, ORE: Occupant Response/ Evacuation, FE: Fire Extension, FD: Fire Damages, BDL: Building Dollar Loss, CDL: Content Dollar Loss, and TDL: Total Dollar Loss. Figure 7-11: ANN-based Simulink model using BR algorithm for prediction of fire impacts efficiently 179 7.3.4. Optimization analysis results 7.3.4.1. Factorial-based response optimization A factorial-based response optimization was used in this study to identify the optimal predictors’ combination that jointly optimizes a set of responses. This optimization helps to evaluate the impact of multiple predictors on a response. Figure 7-12 shows that most of the correlations are nonlinear, indicating the presence of the main effect. The key results of the response optimization found the best combination set of predictors to minimize fire impacts. The optimal set of the predictors were found to be involving a smoke alarm device (code = 1), direct verbal report to a fire station (code = 5), fire extinguished by the occupant (code = 1), and handheld extinguisher (code = 1). Note: ID: Initial Detection, PSAD: Performance of Smoke Alarm Device, TAFD: Transmission of Alarm to Fire Department, AT: Action Taken, MFCE: Method of Fire Control and Extinguishment, FE: Fire Extension, FD: Fire Damages, BDL: Building Dollar Loss, CDL: Content Dollar Loss, and TDL: Total Dollar Loss. Figure 7-12: Main effects for BDL and multiple response prediction optimizations 180 Smoke alarm as a self-contained device was found to be the best initial detection with an activation performance of a smoke alarm device. In addition, a direct verbal report to a fire station was determined to be the optimal solution for transmission of alarm to the fire department. Extinguishing fire by the occupant is the best action to be taken in decreasing the fire impact level. Furthermore, handheld extinguisher as found to be an effective and efficient method of fire control and extinguishment. 7.3.4.2. Artificial neural network and genetic algorithm A genetic algorithm optimization was used to identify the optimal safety strategies (OSS) that jointly optimizes a set of responses in reducing fire impacts. Figure 7-13 shows the first 10 best scenarios in controlling fire impacts. The first optimal set of the predictors involve all the first factors of the predictors (code = 1), including the smoke alarm device, fire extinguished by the occupant, handheld extinguisher, extinguishers and standpipe system, complete sprinkler protection - supervised or watchman service, fixed system other than sprinkler - supervised or watchman service, and coded signal municipal fire alarm system (included are the telegraph or telephone systems, radio systems, and the auxiliary connection to them). 181 Note: OSS: optimal safety strategies, AT: Action Taken, MFCE: Method of Fire Control and Extinguishment Group, TAFD: Transmission of Alarm to Fire Department, MFPF: Manual Fire Protection Facilities, SP: Sprinkler Protection, FSOS: Fixed System Other than Sprinklers. With respect to the first question, different key potential factors of fire protection and intervention strategies for SG-MURBs were investigated. This investigation is comprehensive to enhance and invest in appropriate fire protection and intervention strategies to minimize fire impacts. The strategies cover initial detection, action taken, method of fire control and extinguishment group, manual fire protection facilities, sprinkler protection, fixed system other than sprinklers, and transmission of alarm to fire department. The discovered results for the most used fire safety strategies in SG-MURBs support the claim of identifying trends in fire incidents that help in identifying target hazards and enhance fire and life safety education programs [460][91]. The top strategy that detects 02468ID TAFD AT MFCE MFPF SP FSOSFactorsOSS1 OSS2 OSS3132224422421423243114114122133232175676417661521215244215642426213125531312431234567891 01 1THE OPTIMAL SAFETY STRATEGIES COMBINATION FOR LOW FIRE IMPACTS (NUMBERS ARE SAFETY CODES)SCENARIOSID TAFD AT MFCE MFPF SP FSOS Figure 7-13: Results of genetic algorithm-based optimization 182 fires the most, 58% of the fires, was visual sighting. However, just 28% of the fires were extinguished by the occupant, that could be due to different reason, including the absence of extinguisher for immediate fire suppression. According to the national fire code-Canada, “portable extinguishers shall be located in or adjacent to corridors or aisles that provide access to exits. The absence of portable extinguishers in SG-MURBs might be the reason behind having just 17% of the fires handheld extinguished, which is almost 1/3 of the detected fires visually. Therefore, enhancement and enforcement of protection strategies that would help to intervene quickly, such as installing extinguishers, is essential at SG-MURBs. This would aid in intervening effectively and efficiently to extinguish a fire. This supports the claim of the National Fire Protection Association (NFPA 10 - Table 13.6.1.2) in requiring the installation of fire extinguishers regardless of the presence of any fixed fire suppression systems. Regarding the second question to determine the most effective fire safety strategies to have very low fire impacts, it is found that 12% of the fires with visual sighting have high TDL. This might be due to three main reasons; first, no available adequate suppression systems, and this supports the claim of installing an appropriate extinguisher at the appropriate location, supporting the claim of the National Fire Protection Association. Besides, it supports the given statement of the first question. Second, the fire was not detected early; new technology would be a strong asset in detecting fires and enhancing visual sighting by sending pictures of fire to the owner or show live video; also, the technology could help initiate remote suppression action. Third, awareness of the effective intervention strategies, fire training the occupants is lacking, which might require enhance fire protection training among the tenants. Furthermore, 10% of the fires had a problem with the smoke alarm device, including non-activation and the absence of the device. This requires policy enforcement of installing and inspecting smoke alarm devices. 183 Moreover, 18% of fire department actions had high total dollar loss, 17% of the automatic suppression actions had high TDL, and just 1.2% of the actions that were taken by the occupants had high TDL. This supports all the strategies that could help intervene effectively and efficiently, effectively in terms of extinguishing the origin of the fire and using the appropriate methods and materials. Efficiently, in terms of controlling fire in a short-time as each second counts for fire impact control. Moreover, it supports the requirement of installing extinguishers at SG-MURBs as their usage records the low level of fire damages, extensions, and dollar losses compared with other methods of fire control. It was found that fire suppression using extinguishers in SG-MURBs is ten times better than firefighter’s intervention in reducing dollar loss. Easiness to handle, the ability to attack fire origin, and low response time could be the main reasons behind the effectiveness of portable extinguishing systems. The third question was to predict fire impacts using fire protection and intervention strategies. The predictors were found to be well correlated with the responses of fire impacts; the ANN model was developed with a determination coefficient of 85% and a correlation coefficient of 92%. Consequently, knowing the available strategies for fire detection, transmission, action, and control in SG-MURBs leads to very strong fire impacts prediction. Consequently, according to the results, installing an effective and efficient system for protection and intervention would help to control 85% of fire incidents. Moreover, specific responses could be predicted effectively based on available fire protection and intervention strategies using the Simulink. Consequently, protection and intervention strategies could be improved accordingly. Concerning the fourth question to select the optimal combination of predictors that minimize fire impacts; the best combination set of the predictors to minimize fire impacts 184 to a very low level were determined, including smoke alarm device, direct verbal report to a fire station, extinguish by an occupant, and handheld extinguisher. Selecting these strategies helps the decision-makers in enforcing and using the appropriate fire safety strategies for better controlling fire impacts. This supports the claim of Cowlard et al. [117] where the decision-makers should consider all potential strategies in enhancing fire protection. Thus, improving and enforcing the determined strategies would definitely enhance the fire protection of people, properties, and the environment. Consequently, a new concept is proposed called an intervention-zero building (IZB) to indicate self-sufficient buildings during fire intervention (SSB-FI). 7.4. Summary This chapter developed an assessment approach to investigate, predict, and optimize fire protection and intervention strategies in SG-MURBs. The developed approach was applied to BC. The results found that the top three factors to detect fires are visual sighting, smoke alarm device, and smoke detector device with 58%, 16%, and 7%, respectively. Smoke alarm device was not activated for 2% of the occurred fires, and it was not installed for 8% of the occurred fires. Regarding the action taken, fire departments were able to extinguish 54% of the fires, whereas the occupants extinguished 28%, and just 6% of the fires were extinguished by an automatic system. Concerning the catastrophic fire extension, 8% of the fires were extended beyond the building of origin. Moreover, 30% of the fires were found to have damages beyond the building of origin. Regarding the building dollar loss, 26% of the fires had a very high dollar loss, and 14% had a high dollar loss. It was found that 20% of the fires had high content dollar loss. Furthermore, 18% of the fires had a high total dollar loss. Furthermore, the correlation among the predictors and the responses was statistically significant as a p-value < 0.05 and R > 0.5. Interestingly, the recorded total dollar loss with occupant's actions was 15 times less than fire department interventions and 185 14 times less than automatic suppression. The developed ANN model was found to have a very strong ability (with R2 of 85%) to predict fire impacts using the BR algorithm. An advanced script and MATLAB function were generated to produce results and solve similar problems. Additionally, the ANN Simulink diagram was generated to simulate ANN as an efficient technique to visualize the possible scenarios of the responses. Results of factorial-based response and genetic algorithm optimization determined the optimal set of the predictors for very low fire impacts, combining smoke alarm device, direct verbal report to a fire station, occupant extinguishing, and handheld extinguisher. Consequently, the approach determined the key potential factors of fire protection and intervention strategies for SG-MURBs and selected the most effective fire safety strategies. Furthermore, the approach was able to predict fire impacts using fire protection and intervention strategies and determined the optimum combination of predictors that cooperatively optimize fire impacts. This could help to improve fire protection and intervention strategies accordingly. Moreover, the proposed approach is generalizable, and it could be implemented in other provinces around the world. 186 CHAPTER 8: CONCLUSIONS AND RECOMMENDATIONS 8.1. Summary and Conclusions A summary of this study and the main conclusions are presented in the following sections from 8.1.1 to 8.1.4. 8.1.1. Ignition sources assessment An integrated quantitative fire risk assessment approach was developed to assess the potential ignition sources and their financial impacts in SG-MURBs. The approach helped discover 40 potential ignition source factors that cause fire incidents (ISFs-FIs) in SG-MURBs. It was found that cooking equipment, smoker's material, and open flame to be the top factors with high relative frequencies in all covered cities. Besides, the related dollar loss to the ISFs-FIs was identified. It was found that the relative frequencies of the most potential ISFs-FIs and the related dollar loss vary significantly from one city to another. Consequently, fire prevention and protection strategies should be enhanced accordingly, such as avoiding or removing critical ISFs-FIs and installing the appropriate fire suppression systems. Benchmarks of relative frequencies and total dollar loss for ISFs-FIs were developed; this helps compare and prioritize the city's factors based on their criticality. Furthermore, these would help compare the cities; consequently, Vancouver was the critical city with 58% of ISFs-FIs having a VH-risk level. An artificial neural network model was developed with 99% prediction ability. Fire prevention strategies can be improved accordingly, including installing and using the ignition of sources, education and awareness of residents, and installing suppression materials. 8.1.2. Human reliability: human error analysis This study developed a fire risk assessment approach to quantitatively assess human error factors that induce fire incidents (HEFs-FIs). The approach included five main steps: (i) identified important HEFs-FIs contributing to fire incidents in SG-MURBs and determined 187 their relative frequencies; (ii) defined the appropriate probability distribution for each HEF-FIs based on the relative frequencies; (iii) developed benchmarks to evaluate the HEFs-FIs in several cities; (iv) assessed the contribution of HEFs-FIs to the regional fire incidents using evidential reasoning; and (v) developed ANN and classifier models to predict HEFs-FIs and fire origin. The developed approach was applied in seven cities of BC, Canada, to show its applicability. As a result, thirty-five HEFs were found to be sources of fire incidents in SG-MURBs. About 43% of these HEFs-FIs were identified following Gamma distribution. The most critical HEFs-FIs were analyzed for each city using benchmarking based on different percentiles of distributions. The evidential reasoning results found that 20.68% of the HEFs-FIs were under the “very high” grade, including incendiary fire, suspicious, and smokers' materials, requiring immediate intervention to enhance fire incident prevention in SG-MURBs. The BR-based ANN model was found to be very strongly correlated with R equal to 85% and has an ability with an R2 value of 72% in predicting possible HEFs-FIs and their origin location based on the defined predictors. Moreover, ANN models were found to outperform the classifiers. The results help decision-makers take actions accordingly for prevention and protection purposes through education and awareness of residents and the installation of suppression materials. 8.1.3. Fire protection: assessment, selection, and prediction The fire risk assessment and protection (FRAP) approach was developed based on combustible materials in SG-MURBs. The approach developed benchmarks to assess fire risks and guides in selecting an appropriate method of fire control using suitable fire suppression materials. Besides, the ANN model was developed to predict the impacts of fire in SG-MURBs. The results identified 36 combustible materials ignited in fire incidents (CMs-FIs) in SG-MURBs. Benchmarks for relative frequencies of CMs-FIs and total financial loss were established. The most critical CMs-FIs for each city were determined 188 and ranked. For example, in Vancouver, approximately 24% and 42% of CMs-FIs are at very high-risk and high-risk levels, respectively. Moreover, approximately 45% of the green features are at a high combustibility level. Appropriate fire suppression materials for the top 10 critical CMs-FIs were found to be water (70%), foam (80%), dry-powder (100%), CO2 (20%), and wet-chemical (70%). The study also showed that extinguishers were the most effective in controlling fire extension, damages, and financial losses. The proposed approach can help decision-makers enhance fire protection in SG-MURBs by avoiding critical CMs-FIs, investing in appropriate safety strategies, and improving the related regulations and codes. 8.1.4. Safety strategies management This study developed an integrated approach to investigate and assess fire safety strategies, including protection and intervention. The proposed approach developed ANN and genetic algorithm models to predict potential fire impacts and recommend optimal fire strategies. The results showed that the visual sighting detected 58% of the fires; besides, smoke alarms were not activated for 2% of the fires. Handheld extinguisher, sprinkler system, standpipe and hose systems were able to extinguish 17%, 4%, and 3% of the fires, respectively. Moreover, 30% of the fires were found to have damages beyond the building of origin. Regarding the dollar loss, 26% of the fires caused a very high level of building dollar loss, 20% had a high level of content dollar loss, and 18% of the fires caused a high total dollar loss level. The investigated variables were found to be statistically significant, with a p-value < 0.05. The proposed ANN model was able to predict fire impacts very strongly, with R equal to 92%. Finally, the optimal set of predictors for minimal fire impacts were determined. These results assist decision-makers to enhance and invest in specific fire safety strategies accordingly. 189 8.2. Originality and Contributions This research delivers unique contributions, as the following sub-sections present, providing a more detailed understanding that would enhance fire prevention, protection, and intervention strategies accordingly. 8.2.1. Potential risk and safety factor identification This study identified potential risk and safety factors in SG-MURBs covering ignition sources, human error factors, combustible materials, and safety strategies to enhance fire prevention, protection, and intervention. The framework discovered 40 (forty) potential ignition source factors that cause fire incidents, covering common, smart, and green factors, including cooking, heating, and electrical distribution equipment. Thirty-five human error factors that induced fire incidents were revealed, including misuse of equipment, incendiary fire, and misuse of a source of ignition. Thirty-six combustible materials ignited in fire incidents in SG-MURBs were identified, such as building components, furniture, furnishings, and flammable liquids; besides, the most common green features considered from ignition and combustibility perspectives. Moreover, safety strategies were investigated in SG-MURBs. This identification makes it easy and effective in defining the appropriate safeguards and increasing the awareness accordingly. Moreover, these results help define specific policies for a specific factor to enhance fire safety in SG-MURBs. 8.2.2. Benchmarks development Benchmarks were developed considering relative frequencies and related total dollar loss for the potential factors of ignition sources, human errors, and combustible materials using real fire incidents data of seven cities. The established benchmarks help decision-makers define the criticality degree of relative fire frequencies and dollar loss for specific risk factors against the typical levels of common cities. The benchmarks would help decision-makers prioritize and take immediate actions for specific risk factors that should be 190 avoided, minimized, or transferred to other parties, such as insurance companies. In addition, decision-makers can use benchmarks to identify the level of the risk factors; compare fire risk factor levels among cities, determine critical factors that require immediate reaction for fire risk, install appropriate fire safety techniques, and identify innovations that would help improve fire safety strategies. 8.2.3. Prediction models Machine learning-based models were developed to predict possible fire impacts. Classification and artificial neural network models were developed using several algorithms. Supervised machine learning-based classification was implemented to explore fire incident data, select potential factors, train models, and assess fire impacts using various classifiers, including decision trees, discriminant analysis, support vector machines, nearest neighbors, and ensemble classification. ANN models were developed based on three main algorithms, including Lavenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) backpropagation algorithms. The ANN models considered several predictors and responses; predictors include combustible materials, methods of fire control, age group categories, sex, and contributing condition; responses include fire damage, content dollar loss, building dollar loss, and nature of casualties. The ANN models have prediction abilities of 65%, 72%, 85%, and 99% in predicting potential fire impacts. The ANN models were found to outperform the classifiers. The ANN models helped detect complex nonlinear relationships among predictors and responses implicitly to enhance prevention, protection, and intervention strategies accordingly. Moreover, the ANN models help to predict different types of fire impacts that help decision-makers prepare and control the critical factors and enhance effectively and efficiently. 191 8.2.4. Optimization-based fire response strategies and best management practices Safety strategies for SG-MURBs were investigated to enhance fire protection and intervention, including initial fire detection, action taken, method of fire control and extinguishment, and transmission of alarm to the fire department. Moreover, optimization techniques, including multiobjective-based genetic algorithms, were used to generate the optimum solutions that reduce fire impact. The optimization solutions help to treat fire risk factors by selecting the best combination strategies to control and mitigate relative frequency, related impacts, and criticality of each factor. 8.3. Limitations The limitations and challenges of this study are mainly related to the focus and the data availability as follows. 8.3.1. Focus on SG-MURBs The focus of this study is on smart-green multi-unit residential buildings; therefore, the developed framework may not be fully applied to other buildings, including commercial buildings, without further investigation. The developed framework is generalizable and can be applied to other cities across the world; however, the related data would be essential for investigating potential fire factors, including ignition sources, human error factors, combustible materials, and safety strategies, with the related impacts for better results. Consequently, the benchmarks, assessment, and prediction would be enhanced, and decision preferences could be adjusted to use models and approaches in different contexts, including the flexibility in adapting the inputs and decision preferences. The developed framework is for the operational SG-MURBs and not including the potential fire factors for SG-MURBs under construction. 192 8.3.2. Data availability and uncertainties More data is needed to improve the performance of machine learning models that were found to have abilities of 65%, 72%, and 85% in predicting several potential impacts. Since the available fire data sources do not clearly specify smart and green features in the database, such as ignition sources and combustible materials, defining and estimating relative frequency is challenging. Overall, data collection from different sources varies in quality and format, particularly smart and green features. Consequently, specific techniques might be needed to integrate qualitative and linguistic inputs to handle data uncertainties. 8.4. Future Research Future research on fire risk management for SG-MURBs would be necessary to investigate more variables that would help enhance prediction and optimization performance, including the potential factors that could affect the variation of fire impacts. Specific techniques would be used to handle problems having both quantitative and qualitative criteria, including smart and green features. While the developed framework focused on empirical models, emerging regression and classification strategies, developing mechanistic models focusing on the potential fire contributors' mechanism would help manage fire risk efficiently. Besides, investigating the correlation of SG-MURBs’ age and the related fires over the years and locales would help enhance comprehensive fire assessment and improve the passive protection of fire codes and regulations accordingly. Designing fire suppression systems using smart algorithms to use the appropriate fire suppression materials based on specific combustibility materials would be an interesting future work to enhance the protection of people, properties, and the environment and save the building's aesthetic. Furthermore, future research on intervention-zero (IZ) for SG-MURBs would be essential to enhance the fire protection of people, properties, and the 193 environment. IZ develops self-sufficient buildings during fire intervention. The IZ would integrate smart technologies to help protect the building in-and-out. Designing smart IZ buildings would be an interesting future work to control fire impacts in SG-MURBs. Furthermore, having smart IZ buildings would minimize firefighters' intervention, and this automatically would minimize firefighters' motor vehicle crashes, which were found to be the second leading cause of death for on-duty firefighters [461]. Besides, firefighters’ response time, including the transfer, the answering, and the processing time, is still a challenge. 194 REFERENCES [1] J. Beck, “Shaping the Future of Construction A Breakthrough in Mindset and Technology,” 2016. [2] “Statistics Canada: Canada’s national statistical agency,” 2018. https://www.statcan.gc.ca/eng/start (accessed Nov. 17, 2018). [3] Statistics Canada, “Evolution of housing in Canada, 1957 to 2014,” 2016. [4] Christoph Blumtritt, “Smart Home,” 2019. [Online]. Available: https://www.statista.com/outlook/279/108/smart-home/canada. [5] World Green Building Council, “6th Annual IFC Climate Business Forum, Hong Kong,” 2020. https://www.worldgbc.org/calendar/6th-annual-ifc-climate-business-forum-hong-kong. [6] C. Hachem, “Impact of neighborhood design on energy performance and GHG emissions,” Appl. Energy, vol. 177, pp. 422–434, 2016, doi: 10.1016/j.apenergy.2016.05.117. [7] UNEPFI, “Green Buildings and the Finance Sector An Overview of Financial Institution Involvement in Green Buildings in North America,” 2010. [8] RBC, “Moving into the Mainstream Green Buildings and LEED,” p. 16, 2011. [9] C. N. Office, “green building council: 2011 Annual Report,” no. 866, 2011. [10] M. Wallhagen, M. Glaumann, and U. Westerberg, “What is a" green" building according to different assessment tools?,” Sustain. Build. 2008, Melbourne, …, 2008, [Online]. Available: /citations?view_op=view_citation&continue=/scholar?hl=en&start=340&as_sdt=0,5&scilib=1&citilm=1&citation_for_view=n2mStcoAAAAJ:bFI3QPDXJZMC&hl=en&oi=p. [11] G. D. Kumara and M. Naik, “Impact of green building concept on sustainability of office buildings-a case study IMPACT OF GREEN BUILDING CONCEPT ON SUSTAINABILITY OF OFFICE BUILDINGS-A CASE STUDY,” no. January, 2018. [12] S. Rutherford, The GReen buildinGS Guide. . [13] A. J. Hoffman, “Overcoming the Social and Green Building,” pp. 390–419, 2008. [14] S. Altomonte, S. Schiavon, M. G. Kent, and G. Brager, “Indoor environmental quality and occupant satisfaction in green-certified buildings,” Build. Res. Inf., vol. 0, no. 0, pp. 1–20, 2017, doi: 10.1080/09613218.2018.1383715. [15] M. S. Ferwati, M. Al, A. Shafaghat, and A. Keyvanfar, “Qatar Sustainability Assessment System ( QSAS ) -Neighborhood Development ( ND ) Assessment Model : Coupling green urban planning and green building design,” J. Build. Eng., vol. 22, no. December 2018, pp. 171–180, 2019, doi: 10.1016/j.jobe.2018.12.006. [16] H. A. Rahardjo and M. Prihanton, “The most critical issues and challenges of fire safety for building sustainability in Jakarta,” J. Build. Eng., vol. 29, no. August 2019, p. 101133, 2020, doi: 10.1016/j.jobe.2019.101133. [17] Public Services and Procurement Canada, “Smart buildings initiative – Greener federal buildings – How the federal government manages property – Federal properties and buildings – About government – Canada.ca,” 2019. https://www.tpsgc-pwgsc.gc.ca/biens-property/intelligents-smart/index-eng.html (accessed Feb. 07, 2020). [18] R. VICARS, “Do Smart Home Devices Present Potential Fire Hazards? | Jensen Hughes,” 2019. https://www.jensenhughes.com/insights/how-do-smart-home-devices-turn-into- 195 potential-fire-hazards (accessed Feb. 07, 2020). [19] T. M. Korman, “Fire safety design and construction considerations for sustainable residential structures,” AEI 2013 Build. Solut. Archit. Eng. - Proc. 2013 Archit. Eng. Natl. Conf., pp. 624–632, 2013, doi: 10.1061/9780784412909.061. [20] B. J. Meacham, B. Poole, J. Echeverria, and R. Cheng, “Fire Safety Challenges of Green Buildings,” 2012. doi: 10.1007/978-1-4614-8142-3. [21] B. J. Meacham, Fire safety challenges of green buildings. . [22] F. Li and M. H. Reiss, “Fire & life safety challenges in sustainable tall building design,” Asia Ascending Age Sustain. Skyscraper City - A Collect. State-of-the-Art, Multi-Disciplinary Pap. Tall Build. Sustain. Cities, Proc. CTBUH 9th World Congr., pp. 502–506, 2012, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_1f7bee2a1466d346458M736c10178163125&database=cpx. [23] W. D. Joyce Sean , Miller Lyle, “Fire Safety in Green Buildings,” 2008. [24] W. K. Chow, “Performance-based approach to determining fire safety provisions for buildings in the Asia-Oceania regions,” Build. Environ., vol. 91, pp. 127–137, Sep. 2015, doi: 10.1016/j.buildenv.2015.04.007. [25] M. Ibrahim et al., “Multi-field and multi-scale characterization of novel super insulating panels/systems based on silica aerogels: Thermal, hydric, mechanical, acoustic, and fire performance,” Build. Environ., vol. 151, no. September 2018, pp. 30–42, 2019, doi: 10.1016/j.buildenv.2019.01.019. [26] Brian Meacham · Brandon Poole Juan Echeverria · Raymond Cheng, Fire Safety Challenges of Green Buildings, no. November. 2012. [27] K. Calder, “Building Smart with Fire Risk Mitigation in New Residential Construction,” BC Hous., 2018. [28] J. A. Sutula and N. L. Ryder, “Quantifying the Hazards of Green Building Construction for Fire Investigation,” Int. Symp. Fire Investig. Sci. Technol., pp. 61–72, 2014, [Online]. Available: https://www.jensenhughes.com/wp-content/uploads/2014/10/Publication-Quantifying-the-Hazards-of-Green-Bldg-Const-for-Fire-Invest-Analysis_Sutula-2014.pdf. [29] B. J. Meacham, “Fire Safety Challenges of Green Buildings,” Arup North Am., no. November, p. 45, 2012, doi: 10.1007/978-1-4614-8142-3. [30] R. M. Tavares, J. M. L. Tavares, and S. L. Parry-Jones, “The use of a mathematical multicriteria decision-making model for selecting the fire origin room,” Build. Environ., vol. 43, no. 12, pp. 2090–2100, 2008, doi: 10.1016/j.buildenv.2007.12.010. [31] X. Zhang, M. Skitmore, and Y. Peng, “Exploring the challenges to industrialized residential building in China,” Habitat Int., vol. 41, pp. 176–184, Jan. 2014, doi: 10.1016/j.habitatint.2013.08.005. [32] Public safety Canada, “National Fire Information Database ( NFID),” 2017. [33] R. Ouache, R. Ruparathna, R. Sadiq, and K. Hewage, “Fire Risk Assessment Model for Residential Buildings Using Bow-tie Method,” no. January, 2018, [Online]. Available: http://c.ymcdn.com/sites/www.cafc.ca/resource/resmgr/10_Research_Reports_(NFID)/UBC_OkanaganNFID_Report.pdf. [34] Canadian Wood Council, Fire Safety Design in Buildings. 1996. [35] T. Korhonen and J. Hietaniemi, “Fire Safety of Wooden Façades in Residential Suburb 196 Multi- Storey Buildings,” 2005. [36] D. Hopkin, “A Review of Fire Resistance Expectations for High-Rise UK Apartment Buildings,” Fire Technol., vol. 53, no. 1, pp. 87–106, 2017, doi: 10.1007/s10694-016-0571-9. [37] “National fire code of Canada, 2015,” p. 2015, 2015. [38] C. Keogh and J. Lord, “Guidelines for Designing Fire Safety in Very Tall Buildings,” Soc. Fire Prot. Eng., 2012. [39] A. Anderson and O. A. Ezekoye, “A comparative study assessing factors that influence home fire casualties and fatalities using state fire incident data,” J. Fire Prot. Eng. , Jan. 2013, doi: 10.1177/1042391512469521. [40] P. Rao, “Fire Risk Analysis of Combustible and Non-Combustible Mid-Rise Residential Building USing CuRisk,” p. 174, 2014. [41] X. Zhang, J. Mehaffey, and G. Hadjisophocleous, “Life risks due to fire in mid- and high-rise, combustible and non-combustible residential buildings,” J. Build. Eng., vol. 8, no. December 2015, pp. 189–197, 2016, doi: 10.1016/j.jobe.2016.11.001. [42] B. Östman, D. Brandon, and H. Frantzich, “Fire safety engineering in timber buildings,” Fire Saf. J., vol. 91, no. April, pp. 11–20, 2017, doi: 10.1016/j.firesaf.2017.05.002. [43] B. Meacham and C. Fifiree, “Fire Safety Challenges with Sustainable Buildings , Materials & Systems,” no. September, 2016. [44] S. Asbury and E. Jacobs, “Dynamic Risk Assessment,” Routledge, 2014. [45] M. A. O. Mydin, W. Arminda, and N. M. Sani, “Fire risk assessment of adaptive re-use of historic shop houses for sleeping accommodations in Malaysia,” MATEC Web Conf., vol. 17, Sep. 2014, doi: 10.1051/matecconf/20141701011. [46] X. Sun and M. Luo, “Fire Risk Assessment for Super High-rise Buildings,” Procedia Eng., vol. 71, no. 1, pp. 492–501, 2014, doi: 10.1016/j.proeng.2014.04.071. [47] J. M. Watts, “Fire Risk Assessment Using Multiattribute Evaluation,” Fire Saf. Sci., vol. 5, pp. 679–690, 1997, doi: 10.3801/IAFSS.FSS.5-679. [48] “London Borough of Camden Fire Risk Assessment (FRA) Building: Mary Green Tower, 1-102 Abbey Road, NW8 0BP Date of FRA: 29,” no. June, 2017. [49] S. Y. Li, G. Tao, and L. J. Zhang, “Fire Risk Assessment of High-rise Buildings Based on Gray-FAHP Mathematical Model,” Procedia Eng., vol. 211, pp. 395–402, 2018, doi: 10.1016/j.proeng.2017.12.028. [50] J. Xin and H. C., “Fire risk assessment of residential buildings based on fire statistics from China,” Fire Technol., vol. 50, pp. 1147–1161, 2014. [51] D. Yung, G. V Hadjisophocleous, and B. Yager, “Case study: the use of FiRECAM to identify cost-effective fire safety design options for a large 40-storey office building Case Study: The Use of FiRECAMTM to Identify Cost-Effective Fire Safety Design Options for a Large 40-Storey Office Building.” Accessed: Oct. 24, 2018. [Online]. Available: http://irc.nrc-cnrc.gc.ca/ircpubs. [52] L. Zhao and V. Beck, “The Definition Of Scenarios For The Cesare-risk Model,” Fire Saf. Sci., vol. 5, pp. 655–666, 1997, doi: 10.3801/iafss.fss.5-655. [53] N. Benichou, A. Kashef, D. Torvi, G. Hadjisophocleous, and I. Reid, “FIERAsystem : A Fire Risk Assessment Model for Light Industrial Building Fire Safety Evaluation,” no. 197 November, p. 32, 2002, doi: 10.4224/20378844. [54] A. Bénichou, N.; Kashef, A.; Reid, I.; Morinville, G.; Bounagui, “FIERAsystem: a fire risk assessment tool to evaluate fire safety in buildings and large spaces,” NRC, 2003. [55] X. Li, X. Sun, C.-F. Wong, and G. Hadjisophocleous, “Effects of Fire Barriers on Building Fire Risk - A Case Study Using CUrisk,” Procedia Eng., vol. 135, pp. 444–453, 2016, doi: 10.1016/j.proeng.2016.01.154. [56] D. Hanea and B. Ale., “Risk of human fatality in building fires: A decision tool using Bayesian networks,” Fire Saf. J. J., vol. 44, pp. 704–710, 2009. [57] G. H. Kristiansson, “On probabilistic assessment of life safety in building on fire. Report,” Lund, 1997. [58] G. Q. Chu, T. Chen, Z. H. Sun, and J. H. Sun, “Probabilistic risk assessment for evacuees in building fires,” Build. Environ., vol. 42, pp. 1283–1290, 2007, doi: 10.1016/j.buildenv.2005.12.002. [59] F. Liu, S. Zhao, M. Weng, and Y. Liu, “Fire risk assessment for large-scale commercial buildings based on structure entropy weight method,” Saf. Sci., vol. 94, pp. 26–40, Apr. 2017, doi: 10.1016/j.ssci.2016.12.009. [60] J. Xin and C. Huang, “Fire risk analysis of residential buildings based on scenario clusters and its application in fire risk management,” Fire Saf. J., vol. 62, no. PART A, pp. 72–78, 2013, doi: 10.1016/j.firesaf.2013.09.022. [61] A. Johansen, P. Eik-Andresen, A. Dypvik Landmark, A. Ekambaram, and A. Rolstadås, “Value of Uncertainty: The Lost Opportunities in Large Projects,” Adm. Sci., vol. 6, no. 3, p. 11, 2016, doi: 10.3390/admsci6030011. [62] E. R. Vaidogas, L. Kisežauskienė, and I. Girnienė, “The risk to structures built near roads and rails used for moving hazardous materials,” vol. 3730, no. August, 2016, doi: 10.3846/13923730.2015.1120773. [63] Michael Firestone, “Guiding principles for Monte Carlo analysis,” no. March, 1997. [64] T. Long, P. Therese, L. John, and J. Morgan, “Best practice guidelines for structural fire resistance design of concrete and steel buildings,” 2010, doi: 10.6028/NIST.TN.1681. [65] M. Abrahamsson, “Uncertainty in quantitative risk analysis-characterisation and methods of treatment,” Lutvdg/Tvbb--1024--Se, p. 88, 2002, [Online]. Available: http://lup.lub.lu.se/record/642153. [66] A. S. Markowski, M. S. Mannan, and A. Bigoszewska, “Fuzzy logic for process safety analysis,” J. Loss Prev. Process Ind., vol. 22, no. 6, pp. 695–702, Nov. 2009, doi: 10.1016/j.jlp.2008.11.011. [67] N. Khakzad, F. Khan, and P. Amyotte, “Dynamic risk analysis using bow-tie approach,” Reliab. Eng. Syst. Saf., vol. 104, pp. 36–44, Aug. 2012, doi: 10.1016/j.ress.2012.04.003. [68] N. Khakzad, F. Khan, and P. Amyotte, “Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network,” Process Saf. Environ. Prot., vol. 91, no. 1–2, pp. 46–53, 2013, doi: 10.1016/j.psep.2012.01.005. [69] H. Pasman and W. Rogers, “Bayesian networks make LOPA more effective, QRA more transparent and flexible, and thus safety more definable!,” J. Loss Prev. Process Ind., vol. 26, no. 3, pp. 434–442, May 2013, doi: 10.1016/j.jlp.2012.07.016. [70] American Institute of Chemical engineers, PROCESS EQUIPMENT RELIABILITY DATA with data tables. 1989. 198 [71] R. Ferdous, F. Khan, R. Sadiq, P. Amyotte, and B. Veitch, “Handling and updating uncertain information in bow-tie analysis,” J. Loss Prev. Process Ind., vol. 25, no. 1, pp. 8–19, Jan. 2012, doi: 10.1016/j.jlp.2011.06.018. [72] R. Ferdous, F. Khan, R. Sadiq, P. Amyotte, and B. Veitch, “Analyzing system safety and risks under uncertainty using a bow-tie diagram: An innovative approach,” Process Saf. Environ. Prot., vol. 91, no. 1–2, pp. 1–18, Jan. 2013, doi: 10.1016/j.psep.2011.08.010. [73] A. P. Harpur, K. E. Boyce, and N. C. McConnell, “An investigation into the circumstances surrounding fatal dwelling fires involving very young children,” Fire Saf. J., vol. 61, pp. 72–82, 2013, doi: http://dx.doi.org/10.1016/j.firesaf.2013.08.008. [74] M. Nilsson, Fire safety evaluation of multifunctional buildings - Special emphasis on antagonistic attacks and protection of sensitive areas. 2013. [75] H. Frantzich, “Uncertainty and Risk Analysis in Fire Safety Engineering,” Dep. Fire Saf. Eng. Lund Inst. Technol., vol. PhD, p. 206, 1998. [76] G. Sehlke and J. Jacobson, “System dynamics modeling of transboundary systems: the bear river basin model.,” Ground Water, vol. 43, no. 5, pp. 722–30, 2005, doi: 10.1111/j.1745-6584.2005.00065.x. [77] S. J. Kenway, P. a. Lant, a. Priestley, and P. Daniels, “The connection between water and energy in cities: a review,” Water Sci. Technol., vol. 63, no. 9, p. 1983, 2011, doi: 10.2166/wst.2011.070. [78] T. Bedford and R. M. Cooke, Probabilistic risk analysis: Foundations and methods. Cambridge: Cambridge University Press, 2001. [79] M. R. V Kodur, P Kumar, “Fire hazard in buildings: review, assessment and strategies for improving fire safety,” PSU Res. Rev., 2019. [80] J. E. M Fernandez-Vigil, BG Rodrıguez, “Fire Safety Strategies to Reduce Mortality in Dwellings Occupied by Elderly People: The Spanish Case,” FIRE Technol., 2020. [81] U. S. F. Administration and N. Fire, “Residential Building Fire Causes,” no. May, 2019. [82] J. Smith, A. Dhinsa, F. Rajabali, A. Zheng, S. Bruin, and I. Pike, “The Epidemiology of Residential Fires Among Children and Youth in Canada,” no. February, 2018, [Online]. Available: https://www.injuryresearch.bc.ca/wp-content/uploads/2018/03/UBC-IAN-PIKE-NFID_Report_0220181.pdf. [83] The Global Change Data Lab, “Death rate from fires and burns, 1990 to 2017,” Our World In Data, 2017. https://ourworldindata.org/grapher/fire-death-rates?tab=table (accessed Jan. 09, 2021). [84] “Regulatory Review | The Use of Combustible Materials in the Building Envelope,” Archit. Inst. Br. columbia, 2017, [Online]. Available: https://aibc.ca/2017/12/regulatory-review-the-use-of-combustible-materials-in-the-building-envelope/. [85] S.-Y. Li, G. Tao, and L.-J. Zhang, “Fire Risk Assessment of High-rise Buildings Based on Gray-FAHP Mathematical Model,” Procedia Eng., vol. 211, pp. 395–402, 2018, doi: 10.1016/j.proeng.2017.12.028. [86] J. Xin and C. Huang, “Fire risk analysis of residential buildings based on scenario clusters and its application in fire risk management,” Fire Saf. J., vol. 62, no. PART A, pp. 72–78, 2013, doi: 10.1016/j.firesaf.2013.09.022. [87] B. Östman, D. Brandon, and H. Frantzich, “Fire safety engineering in timber buildings,” Fire Saf. J., vol. 91, no. April, pp. 11–20, 2017, doi: 10.1016/j.firesaf.2017.05.002. 199 [88] B. Van Weyenberge, X. Deckers, R. Caspeele, and B. Merci, “Development of an Integrated Risk Assessment Method to Quantify the Life Safety Risk in Buildings in Case of Fire,” Fire Technol., vol. 55, no. 4, pp. 1211–1242, 2019, doi: 10.1007/s10694-018-0763-6. [89] W. K. Chow, “Performance-based approach to determining fire safety provisions for buildings in the Asia-Oceania regions,” Build. Environ., vol. 91, pp. 127–137, 2015, doi: 10.1016/j.buildenv.2015.04.007. [90] N. D. Hansen, F. B. Steffensen, M. Valkvist, G. Jomaas, and R. Van Coile, “A fire risk assessment model for residential high-rises with a single stairwell,” Fire Saf. J., vol. 95, no. October 2017, pp. 160–169, 2018, doi: 10.1016/j.firesaf.2017.11.006. [91] G. De Sanctis, “Generic Risk Assessment for Fire Safety Performance Evaluation and Optimisation of Design Provisions,” 2015. [92] M. Gerges, S. Penn, D. Moore, C. Boothman, and C. Liyanage, “Multi-storey residential buildings and occupant’s behaviour during fire evacuation in the UK: Factors relevant to the development of evacuation strategies,” Int. J. Build. Pathol. Adapt., vol. 36, no. 3, pp. 234–253, 2018, doi: 10.1108/IJBPA-08-2017-0033. [93] S. Lamont, “High Rise Buildings with Combustible Exterior Wall Assemblies : Fire Risk Assessment Tool,” no. February, 2018. [94] H. Mi, Y. Liu, W. Wang, and G. Xiao, “An Integrated Method for Fire Risk Assessment in Residential Buildings,” vol. 2020, 2020. [95] N. Benichou, A. H. Kashef, I. Reid, G. V. Hadjisophocleous, D. A. Torvi, and G. Morinville, “FIERAsystem: A Fire Risk Assessment Tool to Evaluate Fire Safety in Industrial Buildings and Large Spaces,” J. Fire Prot. Eng., vol. 15, no. 3, pp. 145–172, Aug. 2005, doi: 10.1177/1042391505049437. [96] N. ; Bénichou et al., “FIERAsystem: a fire risk assessment tool to evaluate fire safety in industrial buildings and large spaces,” J. Fire Prot. Eng., vol. 15, no. 3, pp. 145–172, 2005, doi: 10.1177/1042391505049437. [97] BCH, “2018 BCH Design Guidelines Construction Standards,” 2018. [98] R. C. McKINNON, SAFETY MANAGEMENT SYSTEMS. 2017. [99] Y. Hu, “Research on the Application of Fault Tree Analysis for Building Fire Safety of Hotels,” Procedia Eng., vol. 135, pp. 523–529, 2016, doi: 10.1016/j.proeng.2016.01.092. [100] J. Pei and G. Wang, “Bayesian mutual information reliability model for fire risk assessment of high-rise buildings,” Int. J. Electr. Eng. Educ., 2020, doi: 10.1177/0020720919894197. [101] L. Wu and S. Guo, “Comparison and Analysis of Building Fire Risk Assessment Methods,” Proc. - 2018 3rd Int. Conf. Syst. Reliab. Safety, ICSRS 2018, pp. 381–385, 2019, doi: 10.1109/ICSRS.2018.8688723. [102] A. Rachman and R. M. C. Ratnayake, “Machine learning approach for risk-based inspection screening assessment,” Reliab. Eng. Syst. Saf., vol. 185, no. February, pp. 518–532, 2019, doi: 10.1016/j.ress.2019.02.008. [103] X. Zheng, P. Hu, and J. Gao, “Application of BP neural network in fire risk assessment of comprehensive shopping mall,” Proc. - 2019 Int. Conf. Intell. Comput. Autom. Syst. ICICAS 2019, pp. 416–420, 2019, doi: 10.1109/ICICAS48597.2019.00093. [104] G. Bernardini, Fire Safety of Historical Buildings, no. June. 2017. [105] V. M. Pasculescu, S. M. Radu, E. Ghicioi, D. Pasculescu, and T. Niculescu, “Assessing the safety level of technical equipment operating in potentially explosive atmospheres by using 200 computer modelling,” Int. Multidiscip. Sci. GeoConference Surv. Geol. Min. Ecol. Manag. SGEM, vol. 1, no. 2, pp. 43–50, 2014, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_6142757515139e86b7aM762f10178163171&database=cpx. [106] B. Chorlton and J. Gales, “Fire performance of cultural heritage and contemporary timbers,” Eng. Struct., vol. 201, no. September, p. 109739, 2019, doi: 10.1016/j.engstruct.2019.109739. [107] S. Tan and K. Moinuddin, “Systematic review of human and organizational risks for probabilistic risk analysis in high-rise buildings,” Reliab. Eng. Syst. Saf., vol. 188, no. February, pp. 233–250, 2019, doi: 10.1016/j.ress.2019.03.012. [108] I. Miller, Human Behaviour Contributing to Unintentional Residential Fire Deaths 1997-2003, no. 47. 2005. [109] J. Hidayati and K. Nisak, “Identify the performance of government agencies for city fire prevention and firefight by the balanced scorecard method (journal review),” IOP Conf. Ser. Mater. Sci. Eng., vol. 505, no. 1, Jul. 2019, doi: 10.1088/1757-899X/505/1/012097. [110] S. Rusdiana, A. Rusyana, and T. Raja Ahmad Alfaruqi, “Application of the stochastic transition matrix as a prediction pattern for the occurrence of disasters in Aceh province in 2017-2021,” J. Phys. Conf. Ser., vol. 1490, no. 1, Jun. 2020, doi: 10.1088/1742-6596/1490/1/012055. [111] S.-N. Yuki, E. Itoigawa, and M. Umemoto, “Human error analysis of the fire fighting by self-defense activities in large-scale buildings,” J. Environ. Eng., vol. 79, no. 695, pp. 1–10, Jan. 2014, doi: 10.3130/aije.79.1. [112] J. A. Julius and J. F. Grobbelaar, “Integrating human reliability analysis approaches in the EPRI HRA calculator,” Proc. 8th Int. Conf. Probabilistic Saf. Assess. Manag. PSAM 2006, 2006, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_4a7aa29514405e50865M73302061377553&database=cpx. [113] M. Fernández-Vigil, B. Gil Rodríguez, and J. B. Echeverría Trueba, “Fire Safety Strategies to Reduce Mortality in Dwellings Occupied by Elderly People: The Spanish Case,” Fire Technol., 2020, doi: 10.1007/s10694-020-00972-4. [114] S. Hameury et al., “Fire safety of Façades in medium and high-rise wood building: The French experience,” WCTE 2018 - World Conf. Timber Eng., 2018, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_182f14b4167c1cad081M4f001017816339&database=cpx. [115] “Fire safety in the design, management and use of buildings – Code of practice,” Fire Saf. Des. Manag. use Build. Code Pract., pp. 1–418, Feb. 2017, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_5c1d431516fa1130875M79d710178163146&database=cpx. [116] H. S. Huang, C. H. Su, C. B. Li, C. Y. Lin, and C. C. Lin, “Enhancement of fire safety of an existing green building due to natural ventilation,” Energies, vol. 9, no. 3, Mar. 2016, doi: 10.3390/en9030192. [117] A. Cowlard, A. Bittern, C. Abecassis-Empis, and J. Torero, “Fire safety design for tall buildings,” Procedia Eng., vol. 62, pp. 169–181, 2013, doi: 10.1016/j.proeng.2013.08.053. [118] M. C. Hui, F. S. C. Tsui, and M. C. Luo, “Fire incident characteristics of a densely populated oriental urban city,” Fire Saf. Sci., pp. 363–374, 2005, doi: 10.3801/IAFSS.FSS.8-363. [119] National Fire Protection Association, “Standard for Portable Fire Extinguishers,” 2013. 201 [120] K. Tung Cheuk Fan, “Safety Professionals’ Perceptions to Risk Assessment of Hong Kong Construction Industry,” 2004. [121] J. S. Arendt and D. K. Lorenzo, Evaluating Process Safety in the Chemical Industry. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2000. [122] US Department of Defense, STANDARD PRACTICE FOR SYSTEM SAFETY. 2000. [123] R. Ouache, A. Adham, and A. Rasydan, “Technical Methods for the Risk Assessment at an Industry System: Review Paper,” Int. J. Eng. Res. Technol., vol. 3, no. 2, pp. 1602–1611, 2014, Accessed: Jan. 27, 2015. [Online]. Available: http://www.ijert.org/browse/volume-3-2014/february-2014-edition?download=8218%3Atechnical-methods-for-the-risk-assessment-at-an-industry-systemreview-paper&start=320. [124] D. J. Evans, “An appraisal of underground gas storage technologies and incidents, for the development of risk assessment methodology,” Heal. (San Fr., 2008. [125] R. Kang, G. Fu, and J. Yan, “Analysis of the Case of Fire Fighters Casualties in the Building Collapse,” Procedia Eng., vol. 135, pp. 343–348, 2016, doi: 10.1016/j.proeng.2016.01.140. [126] B. J. Meacham, “2014 ICC Annual Conference Fire Safety Challenges of ‘ Green ’ Systems and Features in Buildings 2014 ICC Annual Conference ( c ) Brian Meacham , WPI , 2014,” no. c, pp. 1–44, 2014. [127] D. Rasbash, EVALUATION OF FIRE SAFETY. 2004. [128] R. Ono and S. B. Da Silva, “An analysis of fire safety in residential buildings through fire statistics,” Fire Saf. Sci., pp. 219–230, 2000, doi: 10.3801/IAFSS.FSS.6-219. [129] Vito Maggiolo, Fire Service Features of Buildings and Fire Protection Systems. Occupational Safety and Health Administration, 2015. [130] O. Harrami and C. McIntyre, “Fire and fire protection in homes and public buildings: An analysis of Swedish fire statistics,” Kemi, no. 1, 2006. [131] M. M. Van Dongen, “T HE F UNCTIONING OF HEEREMA MARINE CONTRACTORS ’ P ROJECT RISK MANAGEMENT & THE USEFULNESS OF QUANTITATIVE RISK ANALYSIS,” 2011. [132] Statistics Canada, “Fire statistics in Canada, Selected Observations from the National Fire Information Database 2005 to 2014,” Ottawa ON, 2017. [133] C. Say and A. Wood, “Sustainable rating systems around the world,” Counc. Tall Build. Urban Habitat J., no. II, pp. 18–29, 2008. [134] C. A. Poveda and M. G. Lipsett, “An integrated approach for sustainability assessment: The Wa-Pa-Su project sustainability rating system,” Int. J. Sustain. Dev. World Ecol., vol. 21, no. 1, pp. 85–98, 2014, doi: 10.1080/13504509.2013.876677. [135] D. Castro-Lacouture, J. A. Sefair, L. Flórez, and A. L. Medaglia, “Optimization model for the selection of materials using a LEED-based green building rating system in Colombia,” Build. Environ., vol. 44, no. 6, pp. 1162–1170, 2009, doi: 10.1016/j.buildenv.2008.08.009. [136] A. Sinha, R. Gupta, and A. Kutnar, “Sustainable Development and Green Buildings,” Drv. Ind., vol. 64, no. 1, pp. 45–53, 2013, doi: 10.5552/drind.2013.1205. [137] P. E. Ken Bland, “Building Green with Wood,” Struct. Mag., no. August, pp. 37–39, 2005. [138] W. S. E. Ismaeel, “Developing a Systemic framework for Applying LEED System Managing Energy and Materials Credits,” pp. 2010–2013, 2013. [139] P. Ravesteyn, H. Utrecht, H. Plessius, H. Utrecht, J. Mens, and H. Utrecht, “Smart Green 202 Campus : How IT can Support Sustainability in Higher Education Smart Green Campus : How IT can Support Sustainability in Higher Education,” no. August 2015, 2014. [140] “What is a Smart Building ?,” 2014. [141] M. April, “Green buildings installed the smart,” pp. 1–4, 2012. [142] J. Califano, “Smart Buildings: The Ultimate Guide,” 2018. https://blog.temboo.com/ultimate-smart-building-guide/. [143] “Investment in building construction, August 2020,” Statistics Canada, 2020. . [144] J. Keays, “Specifying combustible construction in Canada,” 2013. . [145] Canadian Wood Council, “Combustible construction,” 2017. . [146] B. Hodson, “Building construction,” 2020. [147] D. Lobo, “The battle between wood and concrete construction,” 2019. . [148] M. Solutions, “Building with wood,” pp. 1–8. [149] “WOOD CONSTRUCTION TECHNOLOGIES IN QUEBEC , CANADA.” [150] CANADIAN WOOD-FRAME HOUSE CONSTRUCTION. . [151] T. Green and F. Building, “The Wood Use Strategy for The Wood Charter Construction : A First Step,” no. February 2008, 2013. [152] J. Li, “Wood Frame Design and Construction,” 2013. [153] C. Wood and C. Conseil, “Wood-Frame Construction Meeting the Challenges of Earthquakes,” no. 5. [154] “Wood construction strategy,” pp. 1–16, 2014. [155] “An Investigation into the Use of Wood vs . Steel and Concrete in APSC 261 : Technology and Society I Presented to : Prepared by : Fardin Barekat,” 2010. [156] “Smart Buildings Survey_ Safety, Security Outrank Going Green.” . [157] M. Lacinák and J. Ristvej, “Smart city , Safety and Security,” Procedia Eng., vol. 192, pp. 522–527, 2017, doi: 10.1016/j.proeng.2017.06.090. [158] O. Omar, “Intelligent building , definitions , factors and evaluation criteria of selection,” Alexandria Eng. J., vol. 57, no. 4, pp. 2903–2910, 2018, doi: 10.1016/j.aej.2018.07.004. [159] O. Soyinka, K. Wai, M. Siu, T. Lawanson, and O. Adeniji, “Assessing smart infrastructure for sustainable urban development in the Lagos metropolis ☆,” J. Urban Manag., vol. 5, no. 2, pp. 52–64, 2017, doi: 10.1016/j.jum.2017.01.001. [160] D. T. Monitor, “Smart Building : Energy efficiency application Smart Building : Energy efficiency application,” no. October, 2017. [161] E. Juntunen, E. Sarjanoja, J. Eskeli, H. Pihlajaniemi, and T. Österlund, “Smart and dynamic route lighting control based on movement tracking,” Build. Environ., vol. 142, no. March, pp. 472–483, 2018, doi: 10.1016/j.buildenv.2018.06.048. [162] A. Bhati, M. Hansen, and C. M. Chan, “Energy conservation through smart homes in a smart city : A lesson for Singapore households,” Energy Policy, vol. 104, no. September 2016, pp. 230–239, 2017, doi: 10.1016/j.enpol.2017.01.032. [163] Z. Li and Q. Liao, “Economic solutions to improve cybersecurity of governments and smart 203 cities via vulnerability markets,” Gov. Inf. Q., no. October 2016, pp. 0–1, 2017, doi: 10.1016/j.giq.2017.10.006. [164] S. T. Ng, F. J. Xu, Y. Yang, and M. Lu, “A Master Data Management Solution to Unlock the Value of Big Infrastructure Data for Smart, Sustainable and Resilient City Planning,” Procedia Eng., vol. 196, no. June, pp. 939–947, 2017, doi: 10.1016/j.proeng.2017.08.034. [165] Jerry Yudelson, “Green Building Rating Systems Around the World,” 2009. [Online]. Available: https://link.springer.com/content/pdf/bbm%3A978-90-481-2782-5%2F1.pdf. [166] V. Nushi, S. Nixha, and R. Basha, “The importance of sustainable construction assessment for the development of modern education in kosovo,” CESB 2013 PRAGUE - Cent. Eur. Towar. Sustain. Build. 2013 Sustain. Build. Refurb. Next Gener., pp. 2–5, 2013. [167] B. K. Nguyen, “Developing a Framework for Assessing Sustainability of Tall-Building Projects,” Dev. a Framew. Assess. Sustain. Tall- Build. Proj., vol. I, no. August, 2012, [Online]. Available: http://etheses.whiterose.ac.uk/2733/. [168] J. Park, J. Yoon, and K. H. Kim, “Critical review of the material criteria of building sustainability assessment tools,” Sustain., vol. 9, no. 2, 2017, doi: 10.3390/su9020186. [169] F. P. Report, “Report,” no. March, 2016. [170] J. Yudelson, “Sustainable retail development: New success strategies,” Sustain. Retail Dev. New Success Strateg., pp. 1–215, 2010, doi: 10.1007/978-90-481-2782-5. [171] Meltem TÜRKER, “GREEN BUILDING RATING SYSTEMS:,” vol. 5, no. 1976, pp. 265–288, 2010. [172] . XiChen. et al, “Developing a robust assessment system for the passive design approach in the green building rating scheme of Hong Kong,” clean Prod., 2017. [173] M. Fields, “Green Building Certification LEED / DGNB,” Development, pp. 1–10. [174] Dgnb, “DGNB Certification System,” October, pp. 1–26, 2011. [175] “LEED v4 Credits Available For Substitution In LEED Canada LEED v4 Credits Available For Substitution In LEED Canada for New Construction and Major Renovations ( NC ) 2009 and LEED Canada for Core & Shell Development ( CS ) 2009,” pp. 1–7, 2014. [176] C. Leed, “Cornell Rolling Service Doors Comprehensive Guide to Earning LEED ® v4 Credits Cornell Rolling Service Doors Earning LEED ® v4 Credits Applicable Qualifying LEED ® v4 Credits :,” pp. 1–20. [177] Fire Engineering, “Green Building Challenges for the Fire Service,” 2011. . [178] China Green Building, “Ministry of Construction Green Building Evaluation Standard- The ‘Three Star’ System,” 2009. http://chinagreenbuildings.blogspot.com/2009/02/ministry-of-construction-green-building.html. [179] mummy zuhaira, “Green Building Index,” Journey Plan. F., 2013. [180] Green Building Council of South Africa, “TECHNICAL MANUAL GREEN STAR SA EXISTING BUILDING PERFORMANCE PILOT,” 2014. [181] World Green Building Council, “Green building in 2017,” 2018. https://www.worldgbc.org/. [182] Justin Pritchard, “Green’ construction can compromise fire safety, experts suggest,” 2017. . [183] J. Kim et al., “Qualities , Use , and Examples of Sustainable Building Materials,” no. 204 December 1998. [184] Stephen E. Doyle, “Plants: An Essential Component of Green Buildings,” 2013. . [185] G. Manzini, P. Gramazio, S. Guastella, C. Liciotti, and G. L. Baffoni, “The fire risk in photovoltaic installations - Test protocols for fire behavior of PV modules,” Energy Procedia, vol. 82, no. 0, pp. 752–758, 2015, doi: 10.1016/j.egypro.2015.11.805. [186] G. Manzini, P. Gramazio, S. Guastella, C. Liciotti, and G. L. Baffoni, “The fire risk in photovoltaic installations - Checking the PV modules safety in case of fire,” Energy Procedia, vol. 81, no. 0, pp. 665–672, 2015, doi: 10.1016/j.egypro.2015.12.051. [187] B. Brooks, “The ground-fault protection blind spot: A safety concern for larger photovoltaic systems in the United States,” Sol. Am. Board Codes Stand., p. 18, 2012. [188] T. SYLVIA, “Solar system fires are on the rise in the U.S.,” PV magazine USA, 2020. . [189] D. Pratt, “Solar found to be ‘most likely’ cause of fire in latest safety incident,” 2018. https://www.solarpowerportal.co.uk/news/solar_found_to_be_most_likely_cause_of_fire_in_latest_safety_incident. [190] David Pratt, “Solar panel electrical fault leaves man in hospital following roof fire,” 2018. https://www.solarpowerportal.co.uk/news/solar_panel_electrical_fault_leaves_man_in_hospital_after_roof_fire. [191] Martin Holladay, “Three Massachusetts Home Fires Linked to Spray-Foam Installation,” 2011. https://www.greenbuildingadvisor.com/article/three-massachusetts-home-fires-linked-to-spray-foam-installation. [192] Richard Defendorf, “An NZE Project, a Tragic Fire, and a Will to Rebuild,” 2010. https://www.greenbuildingadvisor.com/article/an-nze-project-a-tragic-fire-and-a-will-to-rebuild. [193] Robin Tait, “17 hurt in fire at Vegas’ Monte Carlo,” 2008. http://usatoday30.usatoday.com/news/nation/2008-01-25-vegas-fire_N.htm. [194] J. M, “Modern Building Materials Are Factors in Atlantic City Fires,” 2007. https://www.fireengineering.com/articles/2010/05/modern-building-materials-are-factors-in-atlantic-city-fires.html. [195] Bae Ji-sook, “Haeundae Highrise on Fire - Busan Marine City Burns,” 2010, [Online]. Available: http://koreabridge.net/post/haeundae-highrise-fire-busan-marine-city-burns. [196] “Shanghai apartment fire,” 2010, [Online]. Available: http://archive.boston.com/bigpicture/2010/11/shanghai_apartment_fire.html. [197] Florence Pia G. Yu, “Fire breaks out at Sharjah tower,” 2012. https://gulfnews.com/uae/fire-breaks-out-at-sharjah-tower-1.1014750. [198] A. JACOBS, “Fire Ravages Renowned Building in Beijing,” 2009. https://www.nytimes.com/2009/02/10/world/asia/10beijing.html?_r=1. [199] “ISO 31000 RISK MANAGEMENT,” Standards. . [200] H. Ni, A. Chen, and N. Chen, “Some extensions on risk matrix approach,” Saf. Sci., vol. 48, no. 10, pp. 1269–1278, Dec. 2010, doi: 10.1016/j.ssci.2010.04.005. [201] valentin p. Măzăreanu, “Risk management and analysis: risk assessment (qualitative and quantitative),” 2007. [202] T. Nishino, “Physics-based urban fire spread simulation coupled with stochastic occurrence of spot fires,” Stoch. Environ. Res. Risk Assess., vol. 33, no. 2, pp. 451–463, Feb. 2019, doi: 205 10.1007/s00477-019-01649-3. [203] L. A. Gritzo, R. G. Bill Jr., C. J. Wieczorek, and B. Ditch, “Environmental Impact of Automatic Fire Sprinklers: Part 1. Residential Sprinklers Revisited in the Age of Sustainability,” Fire Technol., vol. 47, no. 3, pp. 751–763, Jul. 2011, doi: 10.1007/s10694-010-0191-8. [204] F. Mirahadi, B. McCabe, and A. Shahi, “IFC-centric performance-based evaluation of building evacuations using fire dynamics simulation and agent-based modeling,” Autom. Constr., vol. 101, pp. 1–16, May 2019, doi: 10.1016/j.autcon.2019.01.007. [205] L. T. Wong and N. K. Fong, “Risk analysis of escape time in buildings of Hong Kong,” 10th Int. Conf. Probabilistic Saf. Assess. Manag. 2010, PSAM 2010, vol. 2, pp. 1107–1115, 2010, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_6e3d6013d1799acb1M7d732061377553&database=cpx. [206] G. C. H. Lui and L. Y. C. Tong, “Survey on total fire safety in residential care homes for elderly persons in Hong Kong,” in WIT Transactions on Information and Communication Technologies, 2010, vol. 43 PART I, pp. PI171–PI182, doi: 10.2495/RISK100161. [207] M. Fernandez-Vigil and B. Echeverria Trueba, “Elderly at Home: A Case for the Systematic Collection and Analysis of Fire Statistics in Spain,” Fire Technol., vol. 55, no. 6, pp. 2215–2244, Nov. 2019, doi: 10.1007/s10694-019-00852-6. [208] C. R. Jennings, “Social and economic characteristics as determinants of residential fire risk in urban neighborhoods: A review of the literature,” Fire Saf. J., vol. 62, no. PART A, pp. 13–19, 2013, doi: 10.1016/j.firesaf.2013.07.002. [209] L. Xiong, D. Bruck, and M. Ball, “Unintentional residential fires caused by smoking-related materials: Who is at risk?,” Fire Saf. J., vol. 90, pp. 148–155, Jun. 2017, doi: 10.1016/j.firesaf.2017.04.020. [210] C. K. Lau, K. K. Lai, Y. P. Lee, and J. Du, “Fire risk assessment with scoring system, using the support vector machine approach,” Fire Saf. J., vol. 78, pp. 188–195, Nov. 2015, doi: 10.1016/j.firesaf.2015.10.003. [211] J. Xin and C. F. Huang, “Fire Risk Assessment of Residential Buildings Based on Fire Statistics from China,” Fire Technol., vol. 50, no. 5, pp. 1147–1161, Sep. 2014, doi: 10.1007/s10694-013-0327-8. [212] H. Mi, Y. Liu, W. Wang, G. Xiao, and E. H. Abdelhameed, “An Integrated Method for Fire Risk Assessment in Residential Buildings,” Math. Probl. Eng., vol. 2020, 2020, doi: 10.1155/2020/9392467. [213] N. D. Hansen, F. B. Steffensen, M. Valkvist, G. Jomaas, and R. Van Coile, “A fire risk assessment model for residential high-rises with a single stairwell,” Fire Saf. J., vol. 95, no. December 2017, pp. 160–169, 2018, doi: 10.1016/j.firesaf.2017.11.006. [214] F. W. Akashah, T. K. Baaki, and S. P. Lee, “Fire risk assessment of low cost high rise residential buildings in Kuala Lumpur: A case study,” J. Des. Built Environ., vol. 17, pp. 124–138, 2017, doi: 10.22452/jdbe.sp2017no1.11. [215] A. Anderson and O. A. Ezekoye, “Quantifying Generalized Residential Fire Risk Using Ensemble Fire Models with Survey and Physical Data,” Fire Technol., vol. 54, no. 3, pp. 715–747, 2018, doi: 10.1007/s10694-018-0709-z. [216] Department for Communities and Local Government Publications, “Fire safety risk assessment,” London, 2006. 206 [217] Federal Emergency Management Agency, “SOCIOECONOMIC FACTORS AND THE INCIDENCE OF FIRE,” Washington, DC, 1997. [218] M. Z. M. Tohir, “Multiple vehicle design fire ccenarios in car parking buildings,” 2015. [219] C. M. Bidgood and P. F. Nolan, “Warehouse fires in the UK involving solid materials,” J. Loss Prev. Process Ind., vol. 8, no. 1, pp. 11–16, 1995, doi: 10.1016/0950-4230(95)90056-U. [220] D. Horasan and M. Horasan, “Quantitative and Qualitative Risk Assessments – A Highly Neglected Methodology 2 . Risk Based Assessment Concepts and Approaches.” [221] K. Huang, “Population and Building Factors That Impact Residential Fire Rates in Large U.S. Cities,” Texas State University, 2009. [222] T. Hakkarainen, “Studies on fire safety assessment of construction products,” no. January 2002, 2002. [223] R. W. Fitzgerald and B. J. Meacham, “Fire Performance Analysis for Buildings,” pp. 305–324, 2017, [Online]. Available: https://www.wiley.com/en-us/Fire+Performance+Analysis+for+Buildings%2C+2nd+Edition-p-9781118657096. [224] Ministry of Community Safety and Correctional Services Ontario, “Comprehensive Fire Safety Effectiveness Model,” 2016. . [225] M. Omidvari, N. Mansouri, and J. Nouri, “A pattern of fire risk assessment and emergency management in educational center laboratories,” Saf. Sci., vol. 73, pp. 34–42, 2015, doi: 10.1016/j.ssci.2014.11.003. [226] M. Kobes, I. Helsloot, B. de Vries, and J. G. Post, “Building safety and human behaviour in fire: A literature review,” Fire Saf. J., vol. 45, no. 1, pp. 1–11, 2010, doi: 10.1016/j.firesaf.2009.08.005. [227] D. B. Matellini, A. D. Wall, I. D. Jenkinson, J. Wang, and R. Pritchard, “Modelling dwelling fire development and occupancy escape using Bayesian network,” Reliab. Eng. Syst. Saf., vol. 114, pp. 75–91, 2013, doi: 10.1016/j.ress.2013.01.001. [228] G. Newfield and D. Watts, “Stage 1 Report Building Code Provisions for Residential Buildings and Identification of Technical and Process Risks,” 2008. [229] A. Alhsni, “Impact of fire on the environment and building sustainability,” 2011. [230] Shao-Hoong Wu, “THE FIRE SAFETY DESIGN OF APARTMENT BUILDINGS,” 2001. [231] A. P. Robbins, “Building Sustainability and Fire-Safety Design Interactions : Scoping Study,” vol. 269, 2012. [232] F. Safety, “Code of Practice for Fire Safety in Buildings,” 2011. [233] a. Camillo, E. Guillaume, T. Rogaume, a. Allard, and F. Didieux, “Risk analysis of fire and evacuation events in the European railway transport network,” Fire Saf. J., vol. 60, pp. 25–36, 2013, doi: 10.1016/j.firesaf.2013.04.004. [234] J. Lundin, Safety in Case of Fire – The Effect of Changing Regulations. 2005. [235] P. Lilly and M. M. Vijayalakshmi, “Fire Accidents in Buildings – Case Studies,” vol. 11, no. 4, pp. 178–184, 2014. [236] G. Yadav, V. Gupta, V. Gaur, and M. Bhattacharya, “Optimized Flame Detection Using Image Processing Based Techniques,” Indian J. Comput. Sci. Eng., vol. 3, no. 2, pp. 202–211, 2012. 207 [237] C. Shih, R. S. Horng, and S. Lee, “Investigation of Lab Fire Prevention Management System of Combining Root Cause Analysis and Analytic Hierarchy Process with Event Tree Analysis,” vol. 2016, 2016. [238] National Fire Protection Association, “BULLETIN » Preventing Construction Site Fires,” 2017. [239] S. Sciences, “Department of Safety Sciences,” no. June, pp. 1–24, 2017. [240] J. Xin and C. Huang, “Fire risk analysis of residential buildings based on scenario clusters and its application in fire risk management,” Fire Saf. J., vol. 62, no. PART A, pp. 72–78, 2013, doi: 10.1016/j.firesaf.2013.09.022. [241] N. Perdell, “Home moves,” Fire Prev. Fire Eng. Journals, Feb. 2006, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_30c22110f2a18b770M502f2061377553&database=cpx. [242] M. Pérez and B. L. R. Binders, “Research and Recommendations on the Chronic Problem of Undetermined and Missing Data in the Causal Factors Sections of the National Fire Incident Reporting System,” WD info, vol. 3, no. 2, p. 2004, 2003, doi: 10.1002/ejoc.201200111. [243] F. P. Association, Fire Safety and Risk Management for the NEBOSH National Certificate in Fire Safety and Risk Management. . [244] C. Lukas et al., “Polystyrene foam insulation with a sustainable flame retardant: Transition update,” Fire Mater. 2015 - 14th Int. Conf. Exhib. Proc., pp. 568–583, 2015, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_M5e283b4d15158f4d8a6M7d5b10178163171&database=cpx. [245] uOttawa, “DESIGN and CONSTRUCTION GUIDELINES,” 2018. [246] N. A. A. Aziz, R. M. Nordin, Z. Ismail, J. Yunus, and N. Hashim, “Dwelling Fire Safety Evacuation (DFSE): A Conceptual Approach,” MATEC Web Conf., vol. 266, Feb. 2019, doi: 10.1051/matecconf/201926605004. [247] “2012 3rd International Conference on Applied Mechanics and Mechanical Engineering, ICAMME 2012,” Appl. Mech. Mater., vol. 249–250, 2013, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_6e3d6013cb0fd5bb6M75fe2061377553&database=cpx. [248] A. J. Fowell, Fire and Flammability of Furnishings and Contents of Buildings. 2019. [249] R. Campell, “Electrical Fires,” Nfpa, no. March, 2017, [Online]. Available: https://www.nfpa.org/News-and-Research/Fire-statistics-and-reports/Fire-statistics/Fire-causes/Electrical-and-consumer-electronics/Electrical. [250] F. Yang, X. Qian, and P. Huang, “Fire safety assessment of underground buildings based on grey relational analysis,” Procedia Eng., vol. 45, pp. 89–95, 2012, doi: 10.1016/j.proeng.2012.08.126. [251] M. J. Hurley and E. R. Rosenbaum, Performance-Based Fire Safety Design. . [252] I. D. B. A.M.Hasofer, V.R.Beck, Risk Analysis in Building Fire Safety Engineering, vol. 106, no. 11. 2002. [253] A. V Mccormick, “Residential Fires in Surrey , B . C .,” no. February, 2009. [254] H. Hajiloo and M. F. Green, “Post-fire residual strength of glass fibre reinforced polymer (GFRP) bars,” Proceedings, Annu. Conf. - Can. Soc. Civ. Eng., vol. 3, pp. 2311–2316, 2016, 208 [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_45f348ab15f4a116579M7eac10178163176&database=cpx. [255] “Standard Test Method for Fire Test of Non-Mechanical Fire Dampers Used in Vented Construction,” Stand. Test Method Fire Test Non-Mechanical Fire Dampers Used Vent. Constr., vol. 04.07, 2013, doi: 10.1520/E2912. [256] Y. F. Yang, L. Zhang, and X. Dai, “Performance of recycled aggregate concrete–filled square steel tubular columns exposed to fire,” Adv. Struct. Eng., vol. 20, no. 9, pp. 1340–1356, 2017, doi: 10.1177/1369433216677603. [257] DCLG, “Housing Fire Safety,” 2013, [Online]. Available: http://www.cornwall.gov.uk/default.aspx?page=33016. [258] P. M. Myers, “Layer of Protection Analysis – Quantifying human performance in initiating events and independent protection layers,” J. Loss Prev. Process Ind., vol. 26, no. 3, pp. 534–546, May 2013, doi: 10.1016/j.jlp.2012.07.003. [259] P. Baybutt, “The role of people and human factors in performing process hazard analysis and layers of protection analysis,” J. Loss Prev. Process Ind., vol. 26, no. 6, pp. 1352–1365, Nov. 2013, doi: 10.1016/j.jlp.2013.08.008. [260] O. F. Thompson, E. R. Galea, and L. M. Hulse, “A review of the literature on human behaviour in dwelling fi res,” Saf. Sci., vol. 109, no. January 2017, pp. 303–312, 2018, doi: 10.1016/j.ssci.2018.06.016. [261] M. Kobes, J. Post, I. Helsloot, and B. De Vries, “Fire risk of high-rise buildings based on human behavior in fires,” in First International Conference on fire Safety of High-rise Buildings., 2008, p. 11. [262] L. Xiong, D. Bruck, and M. Ball, “Human response to non-injury accidental house fires,” Fire Mater., vol. 41, no. 5, pp. 431–440, 2017, doi: 10.1002/fam.2409. [263] O. F. Thompson, E. R. Galea, and L. M. Hulse, “A review of the literature on human behaviour in dwelling fires,” Saf. Sci., vol. 109, pp. 303–312, Nov. 2018, doi: 10.1016/J.SSCI.2018.06.016. [264] U.S. Fire Administration. National Fire Data Center, “Fatal Fires in Residential Buildings,” 2010. [265] M. Gerges, M. Mayouf, P. Rumley, and D. Moore, “Human behaviour under fire situations in high-rise residential building,” Int. J. Build. Pathol. Adapt., vol. 35, no. 1, pp. 90–106, 2017, doi: 10.1108/IJBPA-09-2016-0022. [266] J. San-Miguel-Ayanz, P. Barbosa, G. Schmuck, G. Libertà, and J. Meyer-Roux, “The European Forest Fire Information System,” Proc. 6th Agil., pp. 27–30, 2003. [267] FIRE PREVENTION & INVESTIGATION DIVISION, “Fire Safety Plan,” 2012. doi: 10.1093/nar/29.22.4633. [268] R. B. Occur, “Topical Fire Report Series: Vol. 9, Issue 8: Intentionally Set Fires in Residential Buildings,” vol. 9, no. 8, 2009. [269] E. Health, Fire safety manual. 2013. [270] A. Asgary, A. Ghaffari, and J. Levy, “Spatial and temporal analyses of structural fire incidents and their causes : A case of Toronto , Canada,” Fire Saf. J., vol. 45, no. 1, pp. 44–57, 2010, doi: 10.1016/j.firesaf.2009.10.002. [271] T. Article, “Submission Files Included in this PDF Cover letter Dear Editor , We are pleased 209 to submit our manuscript entitled : ‘ A Quantitative Framework for Assessing Human Error Factors-Induced Fire Incidents in Multi-Unit Residential Buildings ’ for consideration.” [272] M. C. Falvo and S. Capparella, “Safety issues in PV systems: Design choices for a secure fault detection and for preventing fire risk,” Case Stud. Fire Saf., vol. 3, pp. 1–16, 2015, doi: 10.1016/j.csfs.2014.11.002. [273] L. Xiong, D. Bruck, and M. Ball, “Comparative investigation of ‘survival’ and fatality factors in accidental residential fires,” Fire Saf. J., vol. 73, pp. 37–47, 2015, doi: http://dx.doi.org/10.1016/j.firesaf.2015.02.003. [274] U. S. F. Administration and N. Fire, “Residential Building Fires (2013-2015),” vol. 18, no. 1, pp. 1–17, 2017, [Online]. Available: https://www.usfa.fema.gov/data/statistics/. [275] I. Cohen and L. Garis, “Residential Fire Injury and Death Rates in British Columbia A Statistical Analysis Pre and Post 1975,” no. January, 2018. [276] “NCC 2019 Fire Safety in Class 2 and Class 3 residential buildings EFT 2575 Report,” 2019. [277] R. Chagger and D. Smith, “The causes of false fire alarms in buildings,” Bre Brief. Pap., 2014, doi: 10.1016/j.bbalip.2010.02.013. [278] P. E. Daniel J. O’Connor, SFPE Task Group on Human Behavior in Fire CHAIR. 2017. [279] British Automatic Fire Sprinkler Association (BAFSA), “Sprinklers for Safer Living,” 2010. [280] M. Kobes and K. Groenewegen, “Consumer fire safety: European statistics and potential fire safety measures,” Vienna Austrian Fed. Minist. Labor, Soc. Aff. Consum. Prot., vol. 31, no. January, 2009. [281] J. Schulz, D. Kent, T. Crimi, J. L. D. Glockling, and T. R. Hull, “A Critical Appraisal of the UK’s Regulatory Regime for Combustible Façades,” Fire Technol., 2020, doi: 10.1007/s10694-020-00993-z. [282] R. W. Fitzgerald and B. J. Meacham, “Fire Department Extinguishment : Arrival,” pp. 219–235, 2017. [283] D. B. Matellini, A. D. Wall, I. D. Jenkinson, J. Wang, and R. Pritchard, “A Bayesian network model for fire development and occupant response within dwellings,” Proc. IEEE 2012 Progn. Syst. Heal. Manag. Conf. PHM-2012, 2012, doi: 10.1109/PHM.2012.6228863. [284] DCLG, Fire Performance of Green Roofs and Walls, no. August. 2013. [285] Usfa, “Topical Fire reporT SerieS Heating Fires in Residential Buildings.” Accessed: Oct. 25, 2018. [Online]. Available: www.usfa.dhs.gov/statistics/reports/index.shtm. [286] I. Labuda, “Possibilities of applying TRIZ methodology elements ( the 40 Inventive Principles ) in the process of architectural design,” vol. 131, pp. 476–499, 2015, doi: 10.1016/j.proeng.2015.12.443. [287] United States Environmental Protection Agency (EPA), “Sustainable Design and Green Building Toolkit,” EPA Reg. 4, no. June, p. 110, 2013. [288] B. C. Roberts, M. E. Webber, and O. A. Ezekoye, “Development of a multi-objective optimization tool for selecting thermal insulation materials in sustainable designs,” Energy Build., vol. 105, pp. 358–367, 2015, doi: 10.1016/j.enbuild.2015.07.063. [289] B. L. Epstien, A. G. Worthan, and W. Eillott Horner, “Green trade-off: Balancing environmental benefit and health impact of building materials,” Proc. A WMA Indoor 210 Environ. Qual. Probl. Res. Solut. Conf. 2006, vol. 1, pp. 355–365, 2006, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_30c2211124824c8e0M59f82061377553&database=cpx. [290] National Commission on Building and Fire Codes [NCBFC] and National Research Council of Canada [NRCC], “National Building Code of Canada,” no. June 1990, p. 1222, 2010, doi: 10.1016/j.semnephrol.2011.11.012. [291] C. A. Boyle, “Sustainable buildings,” Proc. Inst. Civ. Eng. Eng. Sustain., vol. 158, no. March, pp. 41–48, 2005, doi: 10.1680/ensu.2005.158.1.41. [292] A. goleman, daniel; boyatzis, Richard; Mckee and Perdana, International Fire Code, vol. 53, no. 9. 2018. [293] “2013 International Conference on Civil, Architecture and Building Materials, 3rd CEABM 2013,” Appl. Mech. Mater., vol. 357–360, 2013, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_6e3d6014128967aedM7e992061377553&database=cpx. [294] J. Tidwell and J. J. Murphy, “Bridging the Gap – Fire Safety and Green Buildings,” p. 80, 2010. [295] E. Guillaume, F. Didieux, A. Thiry, and A. Bellivier, “Real-scale fire tests of one bedroom apartments with regard to tenability assessment,” Fire Saf. J., vol. 70, pp. 81–97, 2014, doi: 10.1016/j.firesaf.2014.08.014. [296] UNEP / SETAC, Greening the economy through life cycle thinking. 2012. [297] P. H. Fires and R. Buildings, “Topical Fire Report Series - Portable Heater Fires in Residential Buildings,” vol. 10, no. 3, pp. 1–8, 2010. [298] U.S. CSB, “Metal Dust Explosion and Fire,” U. S. Chem. Saf. Hazard Investig. Board, no. July, pp. 1–31, 2014. [299] A. M. David, “Blazing Ballet Girls and Flannelette Shrouds : Fabric , Fire , and Fear in the Long Nineteenth Century,” vol. 9756, no. August, 2016, doi: 10.1080/14759756.2016.1139382. [300] K. Majeed et al., “Potential materials for food packaging from nanoclay/natural fibres filled hybrid composites,” Mater. Des., vol. 46, pp. 391–410, 2013, doi: 10.1016/j.matdes.2012.10.044. [301] Q. Dong, F. You, and S. Q. Hu, “Investigation of fire protection status for nanjing representative historical buildings and future management measures,” Procedia Eng., vol. 71, pp. 377–384, 2014, doi: 10.1016/j.proeng.2014.04.054. [302] P. Stollard, “Fire from First.” [303] N. E. Khorasani, M. Garlock, and P. Gardoni, “Fire load: Survey data, recent standards, and probabilistic models for office buildings,” Eng. Struct., vol. 58, pp. 152–165, 2014, doi: 10.1016/j.engstruct.2013.07.042. [304] M. N. Ibrahim, M. S. Ibrahim, A. Mohd-Din, K. Abdul-Hamid, R. M. Yunus, and M. R. Yahya, “Fire risk assessment of heritage building - Perspectives of regulatory authority, restorer and building stakeholder,” Procedia Eng., vol. 20, pp. 325–328, 2011, doi: 10.1016/j.proeng.2011.11.173. [305] M. A. Saeed, G. E. Andrews, H. N. Phylaktou, and B. M. Gibbs, “Flame speed and Kst reactivity data for pulverised corn COBS and peanut shells,” J. Loss Prev. Process Ind., pp. 1–8, 2017, doi: 10.1016/j.jlp.2017.03.027. 211 [306] W. K. Chow, “Performance-based approach to determining fire safety provisions for buildings in the Asia-Oceania regions,” Build. Environ., vol. 91, pp. 127–137, 2014, doi: 10.1016/j.buildenv.2015.04.007. [307] J. M. V. Antonio Erias, Cansu Karaka, Corinna Grajetzki, James Carton, Mekalia Paulos, Pirjo Jantunen, Prajwal Baral, Samal Bex, “World Energy Resources 2016,” World Energy Counc. 2016, pp. 6–46, 2016, doi: http://www.worldenergy.org/wp-content/uploads/2013/09/Complete_WER_2013_Survey.pdf. [308] L. Tan, M. Hu, and H. Lin, “Agent-based simulation of building evacuation: Combining human behavior with predictable spatial accessibility in a fire emergency,” Inf. Sci. (Ny)., vol. 295, pp. 53–66, 2015, doi: 10.1016/j.ins.2014.09.029. [309] U.S. CSB, “Investigation Report Refinery Explosion and Fire BP Texas City,” U. S. Chem. Saf. Hazard Investig. Board, pp. 1–341, 2007. [310] T. O. Safety, “Fire Protection and Prevention,” 1926. [311] V. K. Kodur and E. M. Aziz, “Effect of temperature on creep in ASTM A572 high-strength low-alloy steels,” Mater. Struct., pp. 1669–1677, 2015, doi: 10.1617/s11527-014-0262-2. [312] “Doors, Windows and Related Hardware Marking and Application Guide UL and the UL logo are trademarks of UL LLC ©2016 1,” pp. 1–35, 2016. [313] R. Jurdi-hage, C. Giblett, and A. Prawzick, “Risk Factors of Residential Careless Cooking Fires in the City of Regina.” [314] “Fire safety in the home.” [315] S. Henderson, Residential Deep Energy Retrofits in Cold Climates. Elsevier, 2013. [316] H. Ibrahim, “FIRE RISK ANALYSIS IN FLNG PROCESSING FACILITY USING BAYESIAN,” no. May, 2019. [317] H. Sun, Z. Xu, H. Wang, and R. Wang, “A Solar/gas Fired Absorption System for Cooling and Heating in a Commercial Building,” Energy Procedia, vol. 70, pp. 518–528, 2015, doi: 10.1016/j.egypro.2015.02.156. [318] P. Di Modica, G. Gibson, G. Kotsikos, and H. Hoydonckx, “Bio-resin for new bio-composite passive fire protection for off-shore application,” ICCM Int. Conf. Compos. Mater., vol. 2015-July, 2015, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_e394496165ecf26a59M61aa1017816339&database=cpx. [319] T. T. Lie, “ASCE Manual and Report on Engineering Practice No78. Structural fire protection,” ASCE Man. Rep. Eng. Pract. No78, no. 78, pp. 371–376, 1992, doi: 10.1016/B978-0-08-097239-8.00032-5. [320] U.S. CSB, “Case study, Fire at Formosa Plastics Corporation: Evaluating Process Hazards,” U. S. Chem. Saf. Hazard Investig. Board, 2006. [321] NUREG/CR-6850 Supplement 1, Fire Probabilistic Risk Assessment Methods Enhancements, no. September 2010. 2010. [322] E. Kalhor, S. M. Asce, V. Valentin, D. Ph, and M. Asce, “Cost Estimation Framework for Optimal Retrofit Planning to Mitigate Residential Building Vulnerability to Wildfires,” vol. 144, no. 1, pp. 1–12, 2018, doi: 10.1061/(ASCE)CO.1943-7862.0001455. [323] American Petroleum Institute, “Damage Mechanisms Affecting fixed Equipment in the Refining Industry,” Am. Pet. Inst., no. December 2003, 2003, [Online]. Available: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Damage+Mechanisms+ 212 Affecting+Fixed+Equipment+in+the+Refining+Industry#0. [324] IEA, “CO2 Emissions from fuel combustion database documentation,” 2017, [Online]. Available: http://wds.iea.org/wds/pdf/WorldCo2_Documentation.pdf. [325] A. E. Cote and C. C. Grant, “Codes and Standards for the Built Environment,” Saf. Built Environ., pp. 51–66, 2003. [326] L. Garis, P. Maxim, L. Thomas, and K. Mark, “Construction Site Fire Response,” no. March, 2015. [327] R. W. Fitzgerald and B. J. Meacham, “Alarm : Actions After Detection,” pp. 203–217, 2017. [328] A. Zheng, A. Jiang, F. Rajabali, K. Turcotte, L. Garis, and I. Pike, “Examining the Relationship Between Firefighter Injuries and Fatalities in the Built Environment : A case for reducing the risk to firefighters through adequate firefighting experience , working smoke alarms and sprinkler coverage in buildings,” no. May, 2018. [329] B. Karlsson, “Fire Risk Index Method Multistorey Apartment Buildings,” 2002. [330] C. K. RS Walls, R Eksteen, “Appraisal of fire safety interventions and strategies for informal settlements in South Africa,” Disaster Prev. Manag., 2019. [331] Z. L. Y Zhang, L Shen, Y Ren, J Wang, “How fire safety management attended during the urbanization process in China?,” J. Clean. Prod., 2019. [332] K. M. P Sabapathy, A Depetro, “Probabilistic risk assessment of life safety for a six-storey commercial building with an open stair interconnecting four storeys: A case study,” Fire Technol., 2019. [333] R. Dârmon, “Probabilistic Methods to Assess the Fire Risk of an Industrial Building,” Procedia Manuf., 2020. [334] Y. T. Q Sun, “A BIM-based simulation framework for fire safety management and investigation of the critical factors affecting human evacuation performance,” Adv. Eng. Informatics, 2020. [335] A. M. Hasofer, V. R. Beck, and I. D. Bennetts, Risk Analysis in Building Fire Safety Engineering. London: Butterworth-Heinemann, 2006. [336] US Army Corps of Engineers, “Corps Risk Analysis Gateway,” 2014. http://www.corpsriskanalysisgateway.us/riskassessment.cfm. [337] T. Zoltán, “Quantitative and Qualitative Risk Analyses in the Chemical,” 2010. [338] J. Joy and D. Grffiths, National Minerals Industry Safety and Health Risk Assessment. 2007. [339] J. BABU, “Layer of Protection Analysis–An effective tool in PHA,” pp. 1–13, 2007, Accessed: Dec. 03, 2014. [Online]. Available: http://www.cholarisk.com/uploads/Layer of protection analysis effectivetool in PHA.pdf. [340] C. Ramírez-Marengo, J. de Lira-Flores, A. López-Molina, R. Vázquez-Román, V. Carreto-Vázquez, and M. S. Mannan, “A formulation to optimize the risk reduction process based on LOPA,” J. Loss Prev. Process Ind., vol. 26, no. 3, pp. 489–494, May 2013, doi: 10.1016/j.jlp.2012.07.009. [341] M. S. Schmidt, “Villains, victims, and heroes: Accounting for the roles human activity plays in LOPA scenarios,” J. Loss Prev. Process Ind., vol. 30, pp. 256–262, Jul. 2014, doi: 10.1016/j.jlp.2013.12.001. [342] A. Summers, W. Vogtmann, and S. Smolen, “Improving PHA/LOPA by consistent consequence severity estimation,” J. Loss Prev. Process Ind., vol. 24, no. 6, pp. 879–885, 213 Nov. 2011, doi: 10.1016/j.jlp.2011.06.022. [343] C. R. Tapping et al., “Prospective study evaluating technical and clinical outcomes from the use of low profile angioplasty (LOPA) systems.,” Clin. Radiol., vol. 68, no. 6, pp. 562–7, Jun. 2013, doi: 10.1016/j.crad.2012.11.004. [344] K. Bingham, “Integrating HAZOP and SIL/LOPA analysis: Best practice recommendations,” … , Syst. Autom. …, no. October 2004, pp. 5–7, 2004, Accessed: Dec. 03, 2014. [Online]. Available: http://www.keesofferman.nl/calculators/integrating_hazop_and_sil.pdf. [345] A. M. Dowell III, “Layer of protection analysis for determining safety integrity level,” ISA Trans., vol. 37, no. 3, pp. 155–165, Jul. 1998, doi: 10.1016/S0019-0578(98)00018-4. [346] K. First, “Scenario identification and evaluation for layers of protection analysis,” J. Loss Prev. Process Ind., vol. 23, no. 6, pp. 705–718, Nov. 2010, doi: 10.1016/j.jlp.2010.07.008. [347] A. A. Frederickson, “The Layer of Protection Analysis ( LOPA ) method Look for best practices and guidelines on how to use the LOPA method as an alternative to mitigate risks .,” no. 1997, pp. 1–9, 2002. [348] R. Gowland, “The accidental risk assessment methodology for industries (ARAMIS)/layer of protection analysis (LOPA) methodology: a step forward towards convergent practices in risk assessment?,” J. Hazard. Mater., vol. 130, no. 3, pp. 307–10, Mar. 2006, doi: 10.1016/j.jhazmat.2005.07.007. [349] R. W. Johnson, “Beyond-compliance uses of HAZOP/LOPA studies,” J. Loss Prev. Process Ind., vol. 23, no. 6, pp. 727–733, Nov. 2010, doi: 10.1016/j.jlp.2010.05.009. [350] S. Khaleghi, “Fuzzy Risk Assessment and Categorization , based on Event Tree Analysis ( ETA ) and Layer of Protection Analysis ( LOPA ): Case Study in Gas Transport System,” no. September, pp. 417–426, 2013. [351] M. Khalil, M. a. Abdou, M. S. Mansour, H. a. Farag, and M. E. Ossman, “A cascaded fuzzy-LOPA risk assessment model applied in natural gas industry,” J. Loss Prev. Process Ind., vol. 25, no. 6, pp. 877–882, Nov. 2012, doi: 10.1016/j.jlp.2012.04.010. [352] J. D. Marx and K. M. Werts, “Application of F-N curves in API RP 752 building siting studies,” J. Loss Prev. Process Ind., vol. 30, no. 1, pp. 301–306, 2014, doi: 10.1016/j.jlp.2014.04.008. [353] L. Ma, Y. Li, L. Liang, M. Li, and L. Cheng, “A novel method of quantitative risk assessment based on grid difference of pipeline sections,” Saf. Sci., vol. 59, pp. 219–226, Nov. 2013, doi: 10.1016/j.ssci.2013.04.012. [354] U. Hauptmanns, “Semi-quantitative fault tree analysis for process plant safety using frequency and probability ranges,” J. Loss Prev. Process Ind., vol. 17, no. 5, pp. 339–345, Sep. 2004, doi: 10.1016/j.jlp.2004.06.004. [355] H.-Z. Huang, X. Tong, and M. J. Zuo, “Posbist fault tree analysis of coherent systems,” Reliab. Eng. Syst. Saf., vol. 84, no. 2, pp. 141–148, May 2004, doi: 10.1016/j.ress.2003.11.002. [356] F. Ortmeier and G. Schellhorn, “Formal Fault Tree Analysis - Practical Experiences,” Electron. Notes Theor. Comput. Sci., vol. 185, pp. 139–151, Jul. 2007, doi: 10.1016/j.entcs.2007.05.034. [357] D. Wang, P. Zhang, and L. Chen, “Fuzzy fault tree analysis for fire and explosion of crude oil tanks,” J. Loss Prev. Process Ind., vol. 26, no. 6, pp. 1390–1398, Nov. 2013, doi: 10.1016/j.jlp.2013.08.022. 214 [358] D. Huang, T. Chen, and M.-J. J. Wang, “A fuzzy set approach for event tree analysis,” Fuzzy Sets Syst., vol. 118, no. 1, pp. 153–165, Feb. 2001, doi: 10.1016/S0165-0114(98)00288-7. [359] K. Mokhtari, J. Ren, C. Roberts, and J. Wang, “Application of a generic bow-tie based risk analysis framework on risk management of sea ports and offshore terminals.,” J. Hazard. Mater., vol. 192, no. 2, pp. 465–75, Aug. 2011, doi: 10.1016/j.jhazmat.2011.05.035. [360] A. S. Markowski and A. Kotynia, “‘Bow-tie’ model in layer of protection analysis,” Process Saf. Environ. Prot., vol. 89, no. 4, pp. 205–213, Jul. 2011, doi: 10.1016/j.psep.2011.04.005. [361] E. Villani and P. E. Miyagi, “Modeling of Fire Protection Systems in Intelligent Buildings,” no. May, 2014. [362] World Health Organization, “Building security and fire protection,” 2014. [Online]. Available: http://www.who.int/biologicals/expert_committee/Supplement-4-TS-security-fire-protection-ECSPP-ECBS.pdf. [363] Norwegian Oil and Gas Association, “NORWEGIAN OIL AND GAS APPLICATION OF IEC 61508 AND IEC 61511 IN THE NORWEGIAN PETROLEUM,” 2004. [364] L. E. S. Codes, N. Du, and D. E. S. Incendies, “THE 2010 NATIONAL BUILDING AND FIRE CODES BÂTIMENT ET DE PRÉVENTION,” no. November, 2010. [365] B. Singh, P. Rajiv, and M. Chandra, “Lie detection using image processing,” Adv. Comput. …, no. 12, 2015, [Online]. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7324092. [366] V. Vipin, “Image Processing Based Forest Fire Detection,” Int. J. Emerg. Technol. Adv. Eng., vol. 2, no. 2, pp. 87–95, 2012, doi: 10.1109/ICCPCT.2014.7054883. [367] U. A. Emirates, “Copyright © 2011, General Headquarters of Civil Defence, Ministry of Interior, United Arab Emirates,” 2011. [368] P. Patel and S. Tiwari, “Flame Detection using Image Processing Techniques,” Int. J. Comput. Appl., vol. 58, no. 18, pp. 13–16, 2012. [369] I. S. O. Technical, M. Board, I. S. O. Tc, and I. S. O. Tc, “Fire safety in ISO – Information about ISO / TC 92.” [370] “Mid-rise Construction in British Columbia.” [371] “Be Red Cross Ready Fire Prevention & Safety Checklist,” no. 658536, p. 658536, 2009. [372] Y. C. Chiu, W. T. Hong, and N. Chen, “Ultrasonically dispersed dyed water mists as a substitute for colored powders,” Case Stud. Fire Saf., vol. 6, pp. 1–9, 2016, doi: 10.1016/j.csfs.2016.09.001. [373] American Institute of Chemical engineers, Safe Handling of Powders and Bulk Solids. American Institute of Chemical Engineers, 2005. [374] T. A. Bulletin, “Strategic Outcomes Practice Grey Areas for Green,” no. December, 2009. [375] K. Harada et al., Fire Science and Technology 2015. 2015. [376] R. Strand and D. Oughton, Risk and Uncertainty –, no. 9. 2009. [377] M. R. Wirthlin, “Managing Risk and Uncertainty : Traditional Methods and the Lean Enterprise Presented By,” Technology, pp. 1–37, 2006, doi: 10.1017/CBO9781107415324.004. [378] C. M. Harvett, “A STUDY OF UNCERTAINTY AND RISK MANAGEMENT PRACTICE RELATIVE TO PERCEIVED PROJECT COMPLEXITY A thesis submitted 215 for the degree of by,” Tese, no. May, 2013. [379] R. Ardianto, P. Chhetri, and S. Dunstall, “Modelling the likelihood of urban residential fires considering fire history and the built environment: A Markov chain approach,” Proc. - 21st Int. Congr. Model. Simulation, MODSIM 2015, pp. 1154–1160, 2015, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_55776cc2170e3ac98edM716c10178163146&database=cpx. [380] M. Spearpoint and C. Hopkin, “A Study of the Time of Day and Room of Fire Origin for Dwelling Fires,” Fire Technol., vol. 56, no. 4, pp. 1465–1485, Jul. 2020, doi: 10.1007/s10694-019-00934-5. [381] Y. Jafari Goldarag, A. Mohammadzadeh, and A. S. Ardakani, “Fire Risk Assessment Using Neural Network and Logistic Regression,” J. Indian Soc. Remote Sens., vol. 44, no. 6, pp. 885–894, 2016, doi: 10.1007/s12524-016-0557-6. [382] G. Chu, J. Wang, and Q. Wang, “Time-dependent fire risk assessment for occupant evacuation in public assembly buildings,” Struct. Saf., vol. 38, pp. 22–31, 2012, doi: 10.1016/j.strusafe.2012.02.001. [383] G. Chu and J. Sun, “Decision analysis on fire safety design based on evaluating building fire risk to life,” vol. 46, pp. 1125–1136, 2008, doi: 10.1016/j.ssci.2007.06.011. [384] K. Frank, M. Spearpoint, and N. Challands, “Uncertainty in Estimating the Fire Control Effectiveness of Sprinklers from New Zealand Fire Incident Reports,” Fire Technol., vol. 50, no. 3, pp. 611–632, 2014, doi: 10.1007/s10694-012-0297-2. [385] N. D. Hansen, F. B. Steffensen, M. Valkvist, G. Jomaas, and R. Van Coile, “A fire risk assessment model for residential high-rises with a single stairwell,” Fire Saf. J., vol. 95, pp. 160–169, 2018, doi: 10.1016/j.firesaf.2017.11.006. [386] C. Guanquan and S. Jinhua, “Quantitative Assessment of Building Fire Risk to Life Safety OCCURRENCE PROBABILITY,” vol. 28, no. 3, 2008, doi: 10.1111/j.1539-6924.2008.01048.x. [387] S. C. Initiative, “Predictive Modeling of Building Fire Risk Predictive Modeling of Building Fire Risk.” [388] J. Gough, Risk and Uncertainty. 1988. [389] Y. Xia, Z. Xiong, X. Dong, and H. Lu, “Risk assessment and decision-making under uncertainty in tunnel and underground engineering,” Entropy, vol. 19, no. 10, 2017, doi: 10.3390/e19100549. [390] M. Abimbola, F. Khan, N. Khakzad, and S. Butt, “Safety and risk analysis of managed pressure drilling operation using Bayesian network,” Saf. Sci., vol. 76, pp. 133–144, 2015, doi: 10.1016/j.ssci.2015.01.010. [391] L. Mkrtchyan, L. Podofillini, and V. N. Dang, “Bayesian belief networks for human reliability analysis: A review of applications and gaps,” Reliab. Eng. Syst. Saf., vol. 139, pp. 1–16, 2015, doi: 10.1016/j.ress.2015.02.006. [392] L. Podofillini and V. N. Dang, “A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction,” Reliab. Eng. Syst. Saf., vol. 117, pp. 52–64, 2013, doi: 10.1016/j.ress.2013.03.015. [393] M. Q. Raza and A. Khosravi, “A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings,” Renew. Sustain. Energy Rev., vol. 50, pp. 1352–1372, 2015, doi: 10.1016/j.rser.2015.04.065. [394] A. Mazumder and J. Rosenthal, “Statistical Decision Theory : Concepts , Methods and 216 Applications ( Special topics in Probabilistic Graphical Models ) Supervisor : Professor J . Rosenthal Part I : DECISION THEORY - Concepts and Methods,” 2003. [395] M. Beynon, B. Curry, and P. Morgan, “The Dempster ± Shafer theory of evidence : an alternative approach to multicriteria decision modelling,” vol. 28, 2000. [396] R. R. Yager and L. Liu, Classic Works of the Dempster-Shafer Theory of Belief Functions. 2008. [397] R. R. Yager, A. We, and D. Shafer, “Generalized Dempster – Shafer Structures,” IEEE Trans. Fuzzy Syst., vol. 27, no. 3, pp. 428–435, 2019, doi: 10.1109/TFUZZ.2018.2859899. [398] Y. Lin and P. Lee, “Dynamic multi-attribute decision making model with grey number evaluations,” vol. 35, pp. 1638–1644, 2008, doi: 10.1016/j.eswa.2007.08.064. [399] B. Wang, R.-Y. Ke, X.-C. Yuan, and Y.-M. Wei, “China׳s regional assessment of renewable energy vulnerability to climate change,” Renew. Sustain. Energy Rev., vol. 40, pp. 185–195, 2014, doi: 10.1016/j.rser.2014.07.154. [400] E. Bozdag, U. Asan, A. Soyer, and S. Serdarasan, “Risk prioritization in Failure Mode and Effects Analysis using interval type-2 fuzzy sets,” Expert Syst. Appl., vol. 42, no. 8, pp. 4000–4015, 2015, doi: 10.1016/j.eswa.2015.01.015. [401] K. Chen et al., “Grey clustering evaluation based on AHP and interval grey number,” 2019, doi: 10.1108/IJICC-04-2018-0045. [402] N. Xie and S. Liu, “Novel methods on comparing grey numbers,” Appl. Math. Model., vol. 34, no. 2, pp. 415–423, 2010, doi: 10.1016/j.apm.2009.05.001. [403] C. Shih, Y. Hsu, J. Yeh, and P. Lee, “Grey number prediction using the grey modification model with progression technique,” Appl. Math. Model., vol. 35, no. 3, pp. 1314–1321, 2011, doi: 10.1016/j.apm.2010.09.008. [404] C. R. Management, Grey Data Analysis. . [405] C. H. Wei, “GREY STATISTICS METHOD OF TECHNOLOGY SELECTION FOR ADVANCED PUBLIC TRANSPORTATION SYSTEMS – The Experience of Taiwan –,” Int. Assoc. Traffic Saf. Sci., vol. 27, no. 2, pp. 66–72, 2003, doi: 10.1016/S0386-1112(14)60145-X. [406] S. A. Adedigba, F. Khan, and M. Yang, “Dynamic failure analysis of process systems using neural networks,” Process Saf. Environ. Prot., vol. 111, pp. 529–543, 2017, doi: 10.1016/j.psep.2017.08.005. [407] M. Sarbayev, “APPLICATION OF ARTIFICIAL NEURAL NETWORK IN PROCESS SAFETY ASSESSMENT,” Nazarbayev University, 2018. [408] J. Izquierdo, A. Crespo Márquez, and J. Uribetxebarria, “Dynamic artificial neural network-based reliability considering operational context of assets.,” Reliab. Eng. Syst. Saf., vol. 188, no. March, pp. 483–493, 2019, doi: 10.1016/j.ress.2019.03.054. [409] N. Pedroni, E. Zio, and G. E. Apostolakis, “Comparison of bootstrapped artificial neural networks and quadratic response surfaces for the estimation of the functional failure probability of a thermal-hydraulic passive system,” Reliab. Eng. Syst. Saf., vol. 95, no. 4, pp. 386–395, 2010, doi: 10.1016/j.ress.2009.11.009. [410] Y. Ding, F. Weng, and J. Yu, “Applying BP neural network in high-rising buildings fire risk assessment,” 2011 3rd Int. Conf. Adv. Comput. Control. ICACC 2011, no. Icacc, pp. 265–268, 2011, doi: 10.1109/ICACC.2011.6016411. [411] Z. Hamza and T. Abdallah, “Mapping Fault Tree into Bayesian Network in safety analysis 217 of process system,” pp. 1–5, 2015. [412] A. de Ruijter and F. Guldenmund, “The bowtie method: A review,” Saf. Sci., pp. 1–8, 2016, doi: 10.1016/j.ssci.2016.03.001. [413] a. a. Chojaczyk, a. P. Teixeira, L. C. Neves, J. B. Cardoso, and C. Guedes Soares, “Review and application of Artificial Neural Networks models in reliability analysis of steel structures,” Struct. Saf., Oct. 2014, doi: 10.1016/j.strusafe.2014.09.002. [414] V. Gosasang, W. Chandraprakaikul, and S. Kiattisin, “A comparison of traditional and neural networks forecasting techniques for container throughput at bangkok port,” Asian J. Shipp. Logist., vol. 27, no. 3, pp. 463–482, 2011, doi: 10.1016/S2092-5212(11)80022-2. [415] T. Zhou, C. Wu, J. Zhang, and D. Zhang, “Incorporating CREAM and MCS into fault tree analysis of LNG carrier spill accidents,” Saf. Sci., vol. 96, pp. 183–191, Jul. 2017, doi: 10.1016/j.ssci.2017.03.015. [416] R. Ouache, M. N. Kabir, and A. a. J. Adham, “A reliability model for safety instrumented system,” Saf. Sci., vol. 80, pp. 264–273, 2015, doi: 10.1016/j.ssci.2015.08.004. [417] A. Kleyner and V. Volovoi, “Application of Petri nets to reliability prediction of occupant safety systems with partial detection and repair,” Reliab. Eng. Syst. Saf., vol. 95, no. 6, pp. 606–613, 2010, doi: 10.1016/j.ress.2010.01.008. [418] Y. O. Sayad, H. Mousannif, and H. Al Moatassime, “Predictive modeling of wildfires: A new dataset and machine learning approach,” Fire Saf. J., vol. 104, pp. 130–146, Mar. 2019, doi: 10.1016/j.firesaf.2019.01.006. [419] T. Rosqvist, R. Molarius, H. Virta, and A. Perrels, “Event tree analysis for flood protection—An exploratory study in Finland,” Reliab. Eng. Syst. Saf., vol. 112, pp. 1–7, 2013, doi: 10.1016/j.ress.2012.11.013. [420] X. Wu, H. Liu, L. Zhang, M. J. Skibniewski, Q. Deng, and J. Teng, “A dynamic Bayesian network based approach to safety decision support in tunnel construction,” Reliab. Eng. Syst. Saf., vol. 134, pp. 157–168, 2015, doi: 10.1016/j.ress.2014.10.021. [421] Y.-S. Lin, “Life risk analysis in residential building fires,” J. Fire Sci., vol. 22, no. 6, pp. 491–504, Nov. 2004, doi: 10.1177/0734904104043030. [422] D. J. Landoll, A Complete Guide for Performing Security Risk Assessments. . [423] J. Li, J. Pu, K. Ren, G. Zhang, and L. Ding, “Fire smoke characteristics in closed ship cabin: a fire model study,” Int. J. Syst. Assur. Eng. Manag., vol. 7, no. 3, pp. 257–261, 2016, doi: 10.1007/s13198-016-0449-8. [424] M. Frandes, B. Timar, and D. Lungeanu, “A risk based neural network approach for predictive modeling of blood glucose dynamics,” Stud. Health Technol. Inform., vol. 228, pp. 577–581, 2017, doi: 10.3233/978-1-61499-678-1-577. [425] A. Taghi-Molla, M. Rabbani, M. H. Karimi Gavareshki, and E. Dehghani, “Safety improvement in a gas refinery based on resilience engineering and macro-ergonomics indicators: a Bayesian network–artificial neural network approach,” Int. J. Syst. Assur. Eng. Manag., vol. 11, no. 3, pp. 641–654, 2020, doi: 10.1007/s13198-020-00968-x. [426] P. Corporation, “Risk Analysis and Simulation Add-In for Microsoft Excel ®,” vol. 14850, no. 607, 2016. [427] T. Alexandridis, “Fitting data into probability distributions.” [428] “@RISK for Risk Analysis,” 2019. https://www.palisade.com/risk/. 218 [429] “Characteristics of Classifier Types,” The MathWorks, 2020. https://www.mathworks.com/help/stats/choose-a-classifier.html#bunt0ky. [430] MathWorks, “Neural Net Pattern Recognition tool - MATLAB nprtool,” 2008. https://www.mathworks.com/help/deeplearning/ref/nprtool.html (accessed Mar. 25, 2020). [431] MathWorks, “Levenberg-Marquardt backpropagation - MATLAB trainlm,” 2006. https://www.mathworks.com/help/deeplearning/ref/trainlm.html;jsessionid=519e8a3896bcf581a3a1d15c37e9 (accessed Mar. 25, 2020). [432] K. Robinson, “Correlation Analysis,” 2016. . [433] R. VICARS, “Do Smart Home Devices Present Potential Fire Hazards? | Jensen Hughes,” 2019. https://www.jensenhughes.com/insights/how-do-smart-home-devices-turn-into-potential-fire-hazards (accessed May 18, 2020). [434] E. J. CURRAN, “6 Common Fire Hazards Lurking in Your Home (and Simple Ways to Prevent Them) | Mental Floss,” 2018. https://www.mentalfloss.com/article/551865/common-fire-hazards-lurking-your-home-and-simple-ways-prevent-them (accessed May 18, 2020). [435] National Research Council of Canada, National fire code of Canada, no. June 1990. 2010. [436] Building Code of Australia, Essential Safety Measures Maintenance Manual. 2007. [437] B. Griffin, “Effective fire safety education for residential students,” 2011. [438] C. Fu, J. Yang, and S. Yang, “A group evidential reasoning approach based on expert reliability,” Eur. J. Oper. Res., vol. 246, no. 3, pp. 886–893, 2015, doi: 10.1016/j.ejor.2015.05.042. [439] Y. Fu, M. Xie, K. Chin, and X. Ju, “Journal of Loss Prevention in the Process Industries Accident analysis model based on Bayesian Network and Evidential Reasoning approach,” J. Loss Prev. Process Ind., vol. 26, no. 1, pp. 10–21, 2013, doi: 10.1016/j.jlp.2012.08.001. [440] B. Wu, X. Yan, Y. Wang, and C. G. Soares, “An Evidential Reasoning-Based CREAM to Human Reliability Analysis in Maritime Accident Process,” vol. 37, no. 10, pp. 1936–1957, 2017, doi: 10.1111/risa.12757. [441] K. Mokhtari, J. Ren, C. Roberts, and J. Wang, “Decision support framework for risk management on sea ports and terminals using fuzzy set theory and evidential reasoning approach,” Expert Syst. Appl., vol. 39, no. 5, pp. 5087–5103, Apr. 2012, doi: 10.1016/j.eswa.2011.11.030. [442] A. S. A. B. Material, “chapter 2. WOOD,” pp. 2–3. [443] T. Hasler, “Research Collection,” 1986, doi: 10.3929/ETHZ-B-000225616. [444] D. Vose and J. Wiley, Risk Anaysis - A quantitative guide. 2008. [445] S. M. Miri Lavasani, Z. Yang, J. Finlay, and J. Wang, “Fuzzy risk assessment of oil and gas offshore wells,” Process Saf. Environ. Prot., vol. 89, no. 5, pp. 277–294, Sep. 2011, doi: 10.1016/j.psep.2011.06.006. [446] J. Yang and D. Xu, “On the Evidential Reasoning Algorithm for Multiple,” IEEE Trans. Syst. Man Cybern. - Part A Syst. Humans, vol. 32, no. 3, pp. 289–304, 2002. [447] Z. Chen et al., “Use of evidential reasoning and AHP to assess regional industrial safety,” vol. 2015, pp. 1–22, 2018, doi: 10.1371/journal.pone.0197125. [448] “Census Profile, 2016 Census,” Statistics Canada, 2016. https://www12.statcan.gc.ca/census-recensement/2016/dp- 219 pd/prof/details/page.cfm?Lang=E&Geo1=CSD&Code1=5935029&Geo2=CD&Code2=5915&Data=Count&SearchText=West-Kelowna&SearchType=Begins&SearchPR=01&B1=All&TABID=1. [449] J. Ren, “ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging,” Knowledge-Based Syst., vol. 26, pp. 144–153, 2012, doi: 10.1016/j.knosys.2011.07.016. [450] Y. Y. Chen, Y. J. Chuang, C. H. Huang, C. Y. Lin, and S. W. Chien, “The adoption of fire safety management for upgrading the fire safety level of existing hotel buildings,” Build. Environ., vol. 51, pp. 311–319, 2012, doi: 10.1016/j.buildenv.2011.12.001. [451] The Architectural Institute of British Columbia, “Regulatory Review | The Use of Combustible Materials in the Building Envelope,” 2017. https://aibc.ca/2017/12/regulatory-review-the-use-of-combustible-materials-in-the-building-envelope/ (accessed Apr. 27, 2020). [452] RIBA architecture, “Understanding Building Regulations about combustible materials,” 2019. https://www.architecture.com/knowledge-and-resources/knowledge-landing-page/understanding-combustible-materials-building-regulations (accessed Apr. 28, 2020). [453] K. Tillander, “Utilisation of statistics to assess fire risks in buildings,” VTT Publ., pp. 3–224, 2004, [Online]. Available: https://www.engineeringvillage.com/share/document.url?mid=cpx_M3169250a141a3787168M76f92061377553&database=cpx. [454] J. Tidwell and J. J. Murphy, “Bridging the Gap: Fire Safety and Green Buildings. A Fire and Building Safety Guide to Green Construction,” 2010. [455] M. S. Al-Homoud and M. M. Khan, “Assessing safety measures in residential buildings in Saudi Arabia,” Build. Res. Inf., vol. 32, no. 4, pp. 300–305, Aug. 2004, doi: 10.1080/0961321042000221025. [456] M. Fernández-Vigil, B. Gil Rodríguez, and J. B. Echeverría Trueba, “Fire Safety Strategies to Reduce Mortality in Dwellings Occupied by Elderly People: The Spanish Case,” Fire Technol., vol. 56, no. 5, pp. 2257–2281, Sep. 2020, doi: 10.1007/s10694-020-00972-4. [457] R. Mena, M. Hennebel, Y.-F. Li, C. Ruiz, and E. Zio, “A risk-based simulation and multi-objective optimization framework for the integration of distributed renewable generation and storage,” Renew. Sustain. Energy Rev., vol. 37, pp. 778–793, 2014, doi: 10.1016/j.rser.2014.05.046. [458] V. D. Tsoukalas and N. G. Fragiadakis, “Prediction of occupational risk in the shipbuilding industry using multivariable linear regression and genetic algorithm analysis,” Saf. Sci., vol. 83, pp. 12–22, 2016, doi: 10.1016/j.ssci.2015.11.010. [459] J. Carr, “An Introduction to Genetic Algorithms,” 2014. [460] Firehouse, “Fire Investigations and Their Role in Prevention | Firehouse,” 2007. https://www.firehouse.com/prevention-investigation/article/10494012/fire-investigations-and-their-role-in-prevention (accessed Sep. 30, 2020). [461] Kelly Donoughe, Jennifer Whitestone, “Analysis of Firetruck Crashes and Associated Firefighter Injuries in the United States,” 2012, [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3503424/. 220 APPENDICES 10.1. Appendix of Chapter 3 Figure A-3-1: Time-based fire occurrence in Canada Figure A-3-2: Day-based fire occurrence in Canada 01234567817514922329737144551959366774181588996310371111118512591333140714811555162917031777185119251999207321472221229523692443DayFire incidents 221 Figure A-3-3: Date-based fire occurrence in Canada Figure A-3-4: Year-based fire occurrence in Canada [binary] incidents 222 Table A-3-1: Fire extension in Canada per months Fire extension Confined to object of origin Confined to part of room/area of origin Confined to room of origin Confined to floor level of origin Confined to building of origin Extended beyond building of origin Confined to roof Months 1 7.8% 8.8% 9.2% 7.6% 6.4% 9.5% 10.0% 2 7.3% 7.3% 8.4% 9.2% 5.7% 17.5% 5.0% 3 9.2% 9.8% 7.7% 7.3% 8.0% 4.8% 0.0% 4 7.9% 9.5% 7.6% 9.5% 5.2% 6.3% 0.0% 5 8.4% 8.4% 8.2% 7.6% 16.4% 15.9% 35.0% 6 8.5% 8.0% 8.2% 10.3% 5.5% 6.3% 20.0% 7 8.5% 8.2% 9.0% 7.6% 18.7% 12.7% 10.0% 8 8.5% 8.7% 7.7% 6.5% 5.2% 3.2% 5.0% 9 8.4% 7.5% 6.0% 9.5% 6.6% 7.9% 5.0% 10 9.5% 8.2% 8.8% 6.5% 9.3% 12.7% 5.0% 11 7.6% 8.2% 9.9% 9.9% 6.1% 0.0% 0.0% 12 8.4% 7.3% 9.4% 8.4% 6.9% 3.2% 5.0% 223 10.2. Appendix of Chapter 4 Table A-4-1: Details of the five predictors for fire impacts Characteristics of occupants Ignition of clothing or other fabrics (ICOF) Code Type of Fabric or Material that might be Ignited (TFMI) Code Age group Categories (AC) Code Sex (SE) Code Contributing Condition (CC) Code Senior citizens (65 years of age and older) 1 Male 1 Contributing Condition- unknown 10 Bedding or bed linen 35 Cotton 41 Adults (18 – 64 years) 2 Female 2 Asleep at time of fire 11 Mattress 36 Wool 42 Youth (12 to 17 years of age) 3 Unknown 3 Bedridden or other physical handicap 12 Upholstered furniture 37 Other natural fibre 43 Children (11 years and under) 4 Impairment by alcohol, drugs or medication 13 Rugs 38 Other synthetic fibre 45 Unknown 9 Awake and no physical or mental impairment at the time of fire 14 Ignition of clothing or other fabrics - unclassified 39 Mixture of fibres 46 Under restraint or detention 15 Ignition of clothing or other fabrics - not applicable 30 Rubber 47 Too young to react to fire emergency 16 Unknown 09 Plastics or plastic foam 48 Mental handicap - includes senility 17 Type of fabric or material ignited - unclassified 49 Child left unattended 18 Type of fabric or material ignited - not applicable 40 Condition of casualty - unclassified 19 Unknown 09 Non-occupant (bystander, passerby) 20 Note: CC: Represents the one most appropriate condition which contributes to the fire impacts. 224 Table A-4-2: Details for the three responses of fire impacts Probable/Possible Consequence (PC) Code Cause Failure Escape (CFE) Code Nature of Casualties (NC) Code Smoke inhalation 1 Cause of failure to escape – unknown 50 Death 1 Burn 2 Trapped by rapid spreading of fire/smoke - through vertical openings, stairways, elevators 51 Minor injury (less than a 1-day hospital or off work) 2 Physical injury 3 Trapped by rapid spreading of fire/smoke – through horizontal openings 52 Light injury (hospitalized 1-2 days and/or off work 1-15 days) 3 Other 4 High flame spread of combustible interior finish 53 Serious injury (hospitalized 3+ days and/or off work 16+ days) 4 Unknown 9 Building collapse 54 Injury, seriousness unknown 5 Falling debris 55 Unknown/undetermined/other 9 Exit blocked, locked, or obstructed 57 Outdoor fire 58 Cause of failure to escape - unclassified 59 Fell, slipped or tripped 60 Exposed to fire products 61 Trapped (or caught) – type of openings unknown 62 Exposed to hazardous materials or toxic fumes 63 Exposure to hazard, type unknown 64 Not applicable – escaped 80 225 Table A-4-3: Dollar loss related to ISFs-FIs in seven cities Factors Kamloops Kelowna Surrey Vancouver Vernon Victoria West Kelowna 1 $ 264,492.00 $ 287,162.00 $1,598,132.00 $3,182,415.00 $111,300.00 $206,200.00 $ 1,000.00 2 $ - $ - $ 167,700.00 $ 208,000.00 $ - $ 4,600.00 $ - 3 $ - $ - $ 5,580.00 $ 63,875.00 $ - $ 1,130.00 $ - 4 $ - $ - $ 300.00 $ 1,600.00 $ - $ - $ - 5 $ 500.00 $ - $ 440,600.00 $ 113,875.00 $ 100.00 $ - $ 7,000.00 6 $ - $ 20,000.00 $ 300,000.00 $ 383,000.00 $ 5,000.00 $ 200.00 $ 10,000.00 7 $ 850.00 $ - $ - $ 76,550.00 $ - $ - $ - 8 $ 15,000.00 $ 2,000.00 $ 200.00 $ 200,000.00 $ - $ - $ - 9 $ - $ - $ - $ 88,600.00 $ - $ 2,000.00 $ - 10 $ 2,000.00 $ - $ - $ 158,100.00 $ - $ 500.00 $ - 11 $ 43,000.00 $ - $ 3,150.00 $ 25,000.00 $ - $ - $ - 12 $ - $ - $ 127,500.00 $ 203,250.00 $ - $ - $ - 13 $ 31,000.00 $ 21,790.00 $ 18,661.00 $ 563,431.00 $ 2,700.00 $ 5,000.00 $ - 14 $ - $ - $ 15,000.00 $ 130,700.00 $ - $ - $ - 15 $ - $ - $ 1.00 $ 1.00 $ - $ - $ - 16 $ - $ - $ 82,800.00 $ 210,100.00 $ - $ - $ - 17 $ - $1,500,000.00 $ - $ 55,000.00 $ - $ - $ - 18 $ - $ - $ 635,100.00 $ 892,700.00 $ 1,000.00 $ - $ 3,000.00 19 $ 5,000.00 $ - $ 500.00 $ 270,000.00 $ 4,000.00 $ 20,000.00 $ - 20 $ 450,000.00 $ - $ 2,000.00 $ 17,545.00 $ - $ - $ - 21 $ 1,500.00 $ 350,000.00 $ 301,510.00 $ 444,820.00 $ - $ - $ 10,000.00 22 $ - $ - $ 197,900.00 $ 182,600.00 $ - $ - $ - 23 $ 1,500.00 $ 1,000.00 $ - $ - $ - $ - $ - 24 $ - $ - $ 241,000.00 $ 61,250.00 $ 450.00 $ - $ 200.00 25 $ - $ - $ 500.00 $ - $ - $ - $125,000.00 26 $ - $ - $ 167,100.00 $ 818,150.00 $ 500.00 $ 100.00 $ - 27 $ 463,370.00 $1,913,199.00 $1,752,651.00 $1,575,360.00 $760,550.00 $174,429.00 $ 1,900.00 28 $ - $ - $ 964,650.00 $ 774,641.00 $ 71,000.00 $ 10,000.00 $ 6,050.00 29 $ 104,500.00 $ 70,000.00 $ - $ - $ - $ - $ - 30 $ - $ - $ 528,600.00 $ 677,585.00 $ - $ 11,700.00 $125,000.00 31 $ 13,000.00 $ 180,000.00 $ 170,200.00 $ 148,500.00 $ 4,000.00 $ - $ 10,000.00 32 $ 10,000.00 $ - $ 5,000.00 $ 33,250.00 $ 1,000.00 $ - $ - 33 $1,528,624.00 $ 1,500.00 $ 275,225.00 $ 175,215.00 $ 3,000.00 $ - $ - 34 $ 811,448.00 $ 15,000.00 $ - $ - $ - $ - $ - 35 $ - $ - $ 5,500.00 $ 100.00 $ - $ - $ - 36 $ 42,200.00 $ - $ 192,000.00 $ 501,000.00 $ - $135,400.00 $ - 37 $ - $ - $ 9,000.00 $ 170,000.00 $ - $ - $ - 38 $ - $ - $ 79,810.00 $ 50.00 $ - $ - $ - 39 $ - $ - $ 12,500.00 $ 26,200.00 $ - $ 1,000.00 $ - 40 $ 100.00 $ - $ 374,201.00 $2,772,454.00 $ - $ 500.00 $ 500.00 226 10.3. Appendix of Chapter 5 Five types of probability distributions are considered the most fitted in this study based on Akaike Information Criteria (AIC) that is chosen to rank fits; Pearson type V distribution, exponential distribution, Gamma distribution, triangular distribution, and Weibull distribution [426], [427]. Different equations are used for those five probability distributions as follows: 1. Pearson type V distribution A Pearson type V distribution is indicated by Pearson5 (alpha, beta) using alpha and beta parameters to present shape and scale, respectively. Pearson Type 5 equations are as follows 𝑃𝐷𝐹; 𝑓(𝐻𝐸𝐹)𝑖 = 1𝛽Γ(∝)𝑒−𝛽/(𝐻𝐸𝐹)𝑖((𝐻𝐸𝐹)𝑖𝛽⁄ )𝛼+1 Eq(A − 5 − 1) 𝑀𝑒𝑎𝑛 = 𝛽𝛼−1 Eq(A − 5 − 2) 𝑆𝑘𝑒𝑤𝑛𝑒𝑠𝑠 = 4√𝛼 − 2𝛼 − 3 for α > 3 Eq(A − 5 − 3) 𝐾𝑢𝑟𝑡𝑜𝑠𝑖𝑠 = 3(𝛼 + 5)(𝛼 − 2)(𝛼 − 3)(𝛼 − 4) 𝑓𝑜𝑟 𝛼 > 4 Eq(A − 5 − 4) Where the parameters α (𝛼 > 0) and β (β > 0) are continuous shape parameter and continuous scale parameter, respectively. 2. Exponential distribution An exponential distribution is indicated by Exponential (beta) using parameter beta to present the mean of the HEF distribution. Exponential distribution equations are as follows 𝑃𝐷𝐹; 𝑓(𝐻𝐸𝐹)𝑖 =𝑒−(𝐻𝐸𝐹)𝑖/𝛽𝛽 Eq(A − 5 − 5) 227 𝑀𝑒𝑎𝑛 = 𝛽 Eq(A − 5 − 6) 𝑆𝑘𝑒𝑤𝑛𝑒𝑠𝑠 = 2 Eq(A − 5 − 7) 𝐾𝑢𝑟𝑡𝑜𝑠𝑖𝑠 = 9 Eq(A − 5 − 8) 3. Gamma distribution Gamma distribution is indicated by Gamma(alpha,beta). Where alpha and beta parameters are used to present shape and scale, respectively. Gamma distribution equations are as follows 𝑃𝐷𝐹; 𝑓(𝐻𝐸𝐹)𝑖 = 1𝛽Γ(∝)((𝐻𝐸𝐹)𝑖𝛽)𝛼−1𝑒−𝐻𝐸𝐹𝑖/𝛽 Eq(A − 5 − 9) 𝑀𝑒𝑎𝑛 = 𝛽𝛼 Eq(A − 5 − 10) 𝑆𝑘𝑒𝑤𝑛𝑒𝑠𝑠 = 2√𝛼 Eq(A − 5 − 11) 𝐾𝑢𝑟𝑡𝑜𝑠𝑖𝑠 = 3 +6𝛼 Eq(A − 5 − 12) 4. Triangular distribution A triangular distribution is indicated by Triang(minimum,m.likely,maximum) which is defined by a minimum (min), a most likely value (m.likely), and a maximum(max). It can be skewed in either direction, depending on the position of m.likely relative. Triangular distribution equations are as follows 𝑃𝐷𝐹; 𝑓(𝑥) = 2((𝐻𝐸𝐹)𝑖−min)(𝑚.𝑙𝑖𝑘𝑒𝑙𝑦−min)(𝑚𝑎𝑥−min) min ≤ (𝐻𝐸𝐹)𝑖 ≤ m. likely Eq(A − 5 − 13) 𝑓(𝑥) = 2(𝑚𝑎𝑥 − (𝐻𝐸𝐹)𝑖)(𝑚𝑎𝑥 − 𝑚. 𝑙𝑖𝑘𝑒𝑙𝑦)(𝑚𝑎𝑥 − min) m. likely ≤ (𝐻𝐸𝐹)𝑖 ≤ max Eq(A − 5 − 14) 228 𝑀𝑒𝑎𝑛 =𝑚𝑖𝑛 + 𝑚. 𝑙𝑖𝑘𝑒𝑙𝑦 + 𝑚𝑎𝑥3 Eq(A − 5 − 15) 𝑆𝑘𝑒𝑤𝑛𝑒𝑠𝑠 = 2√25𝑓(𝑓2−9)(𝑓2+3)3 2⁄ 𝑤ℎ𝑒𝑟𝑒 𝑓 ≡2(𝑚.𝑙𝑖𝑘𝑒𝑙𝑦−min)𝑚𝑎𝑥−𝑚𝑖𝑛− 1 Eq(A − 5 − 16) 𝐾𝑢𝑟𝑡𝑜𝑠𝑖𝑠 = 2.4 Eq(A − 5 − 17) 5. Weibull distribution A Weibull distribution is indicated by Weibull (alpha,beta) with the parameters; alpha and beta to present shape and scale, respectively. Weibull distribution equations are as follows 𝑃𝐷𝐹; 𝑓(𝐻𝐸𝐹)𝑖 =𝛼(𝐻𝐸𝐹)𝑖𝛼−1𝛽𝛼𝑒−(𝑥 𝛽)⁄𝛼 Eq(A − 5 − 18) 𝑀𝑒𝑎𝑛 = 𝛽Γ (1 +1𝛼) Eq(A − 5 − 19) 𝑆𝑘𝑒𝑤𝑛𝑒𝑠𝑠 = Γ (1 +3𝛼) − 3Γ (1 +2𝛼)Γ(1 +1𝛼) + 2Γ3(1 +1𝛼)[Γ (1 +2𝛼) − Γ2 (1 +1𝛼)]3 2⁄ Eq(A − 5 − 20) 𝐾𝑢𝑟𝑡𝑜𝑠𝑖𝑠=Γ (1 +4𝛼) − 4Γ (1 +3𝛼)Γ (1 +1𝛼) + 6Γ (1 +2𝛼)Γ2 (1 +1𝛼) − 3Γ4 (1 +1𝛼)[Γ (1 +2𝛼) − Γ2 (1 +1𝛼)]2 − Eq(A − 5 − 21) where 𝛤 is the Gamma Function. 229 Figure A-5-1: Occupants in dwelling unit at the time of the fire function [trainedClassifier, validationAccuracy] = trainClassifier(trainingData) % [trainedClassifier, validationAccuracy] = trainClassifier(trainingData) % returns a trained classifier and its accuracy. This code recreates the % classification model trained in Classification Learner app. Use the % generated code to automate training the same model with new data, or to % learn how to programmatically train models. % % Input: % trainingData: a table containing the same predictor and response % columns as imported into the app. % % Output: % trainedClassifier: a struct containing the trained classifier. The % struct contains various fields with information about the trained % classifier. % % trainedClassifier.predictFcn: a function to make predictions on new % data. % % validationAccuracy: a double containing the accuracy in percent. In % the app, the History list displays this overall accuracy score for % each model. % % Use the code to train the model with new data. To retrain your % classifier, call the function from the command line with your original % data or new data as the input argument trainingData. % % For example, to retrain a classifier trained with the original data set % T, enter: % [trainedClassifier, validationAccuracy] = trainClassifier(T) % % To make predictions with the returned 'trainedClassifier' on new data T2, % use % yfit = trainedClassifier.predictFcn(T2) % 34.2%11.8%13.2%9.3%31.4%Incendiary firesMisuse of source of ignitionMisuse of material ignitedMechanical/electrical failure/malfunctionHuman failingADULTS (18 – 64 years)10.4%42.6%47.0%Incendiary firesMisuse of material ignitedHuman failingSENIOR CITIZENS (65 year and older) 230 % T2 must be a table containing at least the same predictor columns as used % during training. For details, enter: % trainedClassifier.HowToPredict % Extract predictors and response % This code processes the data into the right shape for training the % model. inputTable = trainingData; predictorNames = {'ISFsFIs', 'AISFsFIs', 'EP', 'YP', 'LP'}; predictors = inputTable(:, predictorNames); response = inputTable.AFO; isCategoricalPredictor = [false, false, false, false, false]; % Train a classifier % This code specifies all the classifier options and trains the classifier. classificationTree = fitctree(... predictors, ... response, ... 'SplitCriterion', 'gdi', ... 'MaxNumSplits', 20, ... 'Surrogate', 'off', ... 'ClassNames', [1; 2; 4; 5; 6; 7; 8; 9]); % Create the result struct with predict function predictorExtractionFcn = @(t) t(:, predictorNames); treePredictFcn = @(x) predict(classificationTree, x); trainedClassifier.predictFcn = @(x) treePredictFcn(predictorExtractionFcn(x)); % Add additional fields to the result struct trainedClassifier.RequiredVariables = {'ISFsFIs', 'AISFsFIs', 'EP', 'YP', 'LP'}; trainedClassifier.ClassificationTree = classificationTree; trainedClassifier.About = 'This struct is a trained model exported from Classification Learner R2018b.'; trainedClassifier.HowToPredict = sprintf('To make predictions on a new table, T, use: \n yfit = c.predictFcn(T) \nreplacing ''c'' with the name of the variable that is this struct, e.g. ''trainedModel''. \n \nThe table, T, must contain the variables returned by: \n c.RequiredVariables \nVariable formats (e.g. matrix/vector, datatype) must match the original training data. \nAdditional variables are ignored. \n \nFor more information, see How to predict using an exported model.'); % Extract predictors and response % This code processes the data into the right shape for training the % model. inputTable = trainingData; predictorNames = {'ISFsFIs', 'AISFsFIs', 'EP', 'YP', 'LP'}; predictors = inputTable(:, predictorNames); response = inputTable.AFO; isCategoricalPredictor = [false, false, false, false, false]; % Perform cross-validation partitionedModel = crossval(trainedClassifier.ClassificationTree, 'KFold', 5); % Compute validation predictions [validationPredictions, validationScores] = kfoldPredict(partitionedModel); % Compute validation accuracy validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError'); 231 10.4. Appendix of Chapter 6 Green features C1 C2 C3 C4 C5 C6 C7 C8 C9 Mean Max Structural Materials and Systems Fiber reinforced polymer (FRP) elements 3 4 4 2 2 4 2 4 2 3 4 Lightweight concrete 2 2 2 2 2 2 2 2 2 2 2 Lightweight engineered lumber 3 4 4 2 2 4 2 4 2 3 4 Plastic lumber 3 4 4 2 2 4 2 4 2 3 4 Bamboo 3 4 4 2 2 4 2 4 2 3 4 Bio-polymer lumber 3 4 4 2 2 4 2 4 2 3 4 Extended solar roof panels 2 2 2 2 2 2 2 2 4 2 4 Exterior Materials and Systems Structural integrated panel (SIP) 3 3 4 4 3 2 4 4 3 3 4 Rigid foam insulation 4 4 4 4 4 2 4 4 3 4 4 Exterior insulation & finish (EFIS) 3 3 4 4 3 2 4 4 3 3 4 Spray-applied foam insulation 4 4 4 4 4 2 4 4 3 4 4 Foil insulation systems 3 3 3 4 4 2 4 4 3 3 4 High-performance glazing 2 2 2 3 2 2 3 2 3 2 3 Low-emissivity & reflective coating 2 2 2 3 2 2 3 2 3 2 3 Bamboo, other cellulosic 2 2 2 2 2 2 2 4 2 2 4 Double-skin façade / cavity walls 2 2 2 3 2 2 3 2 3 2 3 Bio-polymers, FRPs 2 2 2 2 2 2 2 4 2 2 4 Vegetative roof systems 3 3 3 2 2 2 2 3 2 2 3 Exterior cable / cable trays 2 2 2 2 2 2 2 4 2 2 4 Façade Attributes Area of glazing 2 2 2 2 2 2 2 2 2 2 2 Area of combustible material 2 2 3 3 2 2 2 2 2 2 3 Exterior vegetative covering 2 3 3 2 2 2 2 3 3 2 3 Interior Materials and Finishes FRP walls / finishes 2 3 4 3 3 2 2 4 3 3 4 Bio-polymer wall / finishes 2 3 4 3 3 2 2 4 3 3 4 Glass walls 2 2 2 2 2 2 2 3 3 2 3 Bio-filtration walls 2 3 3 3 3 2 2 3 3 3 3 Wood panel walls / finishes 2 2 2 2 2 2 2 2 2 2 2 Bamboo walls / finishes 2 3 4 3 3 2 2 4 3 3 4 FRP flooring 2 3 4 3 3 2 2 4 3 3 4 Bio-polymer flooring 2 3 4 3 3 2 2 4 3 3 4 Bamboo flooring 2 3 4 3 3 2 2 4 3 3 4 Interior Space Attributes More enclosed spaces 2 2 2 2 3 2 2 2 2 2 3 Higher insulation values 2 2 2 2 3 2 2 2 2 2 3 Tighter construction 2 2 2 2 2 2 2 2 2 2 2 More open space (horizontal) 2 2 2 2 2 2 2 2 2 2 2 Interior vegetation 2 2 2 2 2 2 2 2 2 2 2 More open space (vertical) 2 2 2 2 2 2 2 2 2 2 2 Building Systems & Issues Natural ventilation 2 2 2 2 2 2 2 2 2 2 2 High volume low speed fans 2 2 2 2 2 2 2 2 3 2 3 Refrigerant materials 2 2 2 2 2 2 2 2 2 2 2 Grey-water for suppression 2 2 2 2 2 2 2 2 3 2 3 On-site waste treatment 2 2 2 2 2 2 2 2 2 2 2 On-site water treatment 2 2 2 2 2 2 2 2 2 2 2 On-site cogeneration 2 2 2 2 2 2 2 2 2 2 2 Rain-water for suppression 2 2 2 2 2 2 2 2 3 2 3 Reduced water suppression systems 2 2 2 2 2 2 2 2 3 2 3 Alternative Energy Systems Tankless water heaters 2 2 2 2 2 2 2 2 2 2 2 Cogeneration systems 2 2 4 2 2 2 2 2 2 2 4 Battery storage systems 2 2 3 2 2 2 2 2 2 2 3 Oil-filled PV panels 2 2 4 2 2 2 2 2 2 2 4 PV roof panels 2 4 2 2 2 2 2 2 2 2 4 Table A-6-1: Combustibility assessment of green features [29] with a degree of belief based on three grades L(2), M(3), and H (4) 232 • Advanced script % Solve an Input-Output Fitting problem with a Neural Network % This script assumes these variables are defined: % Inputs2 - input data. % Outputs5 - target data. x = Inputs2; t = Outputs5; % Choose a Training Function % For a list of all training functions type: help nntrain % 'trainlm' is usually fastest. % 'trainbr' takes longer but may be better for challenging problems. % 'trainscg' uses less memory. Suitable in low memory situations. trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation. % Create a Fitting Network hiddenLayerSize = 50; net = fitnet(hiddenLayerSize,trainFcn); % Choose Input and Output Pre/Post-Processing Functions % For a list of all processing functions type: help nnprocess net.input.processFcns = {'removeconstantrows','mapminmax'}; net.output.processFcns = {'removeconstantrows','mapminmax'}; % Setup Division of Data for Training, Validation, Testing % For a list of all data division functions type: help nndivision net.divideFcn = 'dividerand'; % Divide data randomly net.divideMode = 'sample'; % Divide up every sample net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; % Choose a Performance Function % For a list of all performance functions type: help nnperformance net.performFcn = 'mse'; % Mean Squared Error % Choose Plot Functions % For a list of all plot functions type: help nnplot net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ... 'plotregression', 'plotfit'}; % Train the Network [net,tr] = train(net,x,t); 233 % Test the Network y = net(x); e = gsubtract(t,y); performance = perform(net,t,y) % Recalculate Training, Validation and Test Performance trainTargets = t .* tr.trainMask{1}; valTargets = t .* tr.valMask{1}; testTargets = t .* tr.testMask{1}; trainPerformance = perform(net,trainTargets,y) valPerformance = perform(net,valTargets,y) testPerformance = perform(net,testTargets,y) % View the Network view(net) % Plots, % Uncomment these lines to enable various plots. %figure, plotperform(tr) %figure, plottrainstate(tr) %figure, ploterrhist(e) %figure, plotregression(t,y) %figure, plotfit(net,x,t) % Deployment % Change the (false) values to (true) to enable the following code blocks. % See the help for each generation function for more information. if (false) % Generate MATLAB function for neural network for application % deployment in MATLAB scripts or with MATLAB Compiler and Builder % tools, or simply to examine the calculations your trained neural % network performs. genFunction(net,'myNeuralNetworkFunction'); y = myNeuralNetworkFunction(x); end if (false) % Generate a matrix-only MATLAB function for neural network code % generation with MATLAB Coder tools. genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes'); y = myNeuralNetworkFunction(x); end 234 if (false) % Generate a Simulink diagram for simulation or deployment with. % Simulink Coder tools. gensim(net); end • MATLAB Matrix-Only Function function [Y,Xf,Af] = myNeuralNetworkFunction(X,~,~) %MYNEURALNETWORKFUNCTION neural network simulation function. % [Y] = myNeuralNetworkFunction(X,~,~) takes these arguments: % X = 1xTS cell, 1 inputs over TS timesteps % Each X{1,ts} = 2xQ matrix, input #1 at timestep ts. % and returns: % Y = 1xTS cell of 1 outputs over TS timesteps. % Each Y{1,ts} = 5xQ matrix, output #1 at timestep ts. % where Q is number of samples (or series) and TS is the number of timesteps. %#ok<*RPMT0> % ===== NEURAL NETWORK CONSTANTS ===== % Input 1 x1_step1.xoffset = [1;1]; x1_step1.gain = [0.285714285714286;0.285714285714286]; x1_step1.ymin = -1; % Layer 1 b1 = [-10.031197654821855991;9.8633467189797947583;9.1036023530877798748;9.2917426399839264661;-8.4327504032677111923;7.8095463028764759983;7.5773674677648106268;-6.6338833674606627611;-6.3513688301025030825;6.1599777669735811259;5.6363086512180391452;5.6414206372416337842;5.1676065714423744524;-4.6435422741132486024;-3.8164584979558244804;4.17055279522977429;-2.8322841263126741218;2.9298340962356941297;-2.8976792905680741619;1.8718601722037175872;-1.8576214805587338841;2.1137444050589522249;-1.0111756448900244276;-1.3142207406396173575;-0.83277044797428945166;0.25368114672418384803;0.48410165998117293817;-0.52277714125336260942;-1.2097847597679098897;-1.8124535064302058629;-2.246738771668808532;2.6693008341713539977;2.8661106082645337878;-3.4834465902024067674;3.9144087735769708125;4.1706505262637829645;4.9319700791006368945;5.2515877627102405256;-5.4159597372690599926;-6.8035308718685314489;5.9955056519287195371;6.7637881618883461954;6.6949874991653537393;7.3758729166740479855;-7.7115693649004919408;8.3403221944261947129;9.0916174696330838145;9.2405052390554853758;9.4378839155270792816;-9.9952329983001781954]; IW1_1 = [3.6557384489356627988 -9.0839597622090249018;-0.48828683552514468014 -9.5087380425252394645;-9.8306313439002135368 -0.37479335485492437652;-2.2565912886972911977 -8.9420886843553137879;7.3333584057655851751 6.312445077309919661;-8.7754300018884876522 -4.5941121664447006623;-2.4756255300020622023 -9.4764062329309837196;6.9735535866793858517 -7.4727178923738009075;9.1538903076088935507 -3.2778737542303129082;-9.4058987987053743751 4.1159520670309026613;-9.8823144630036878766 -1.7883723161865856532;-2.8968429031304134824 -9.4308912500323387462;-6.7115669418398606894 7.209166476016379832;6.752621053575879273 7.3371550654147528192;8.5930173924147172926 -5.1991590731599020003;-2.3649533256881096754 -9.2750711769511191562;5.3873289827609651681 -8.5089517049389211678;-5.4463486757294745644 8.3688383159957346891;4.7296323196293528213 -8.4740804923379631219;-8.594013523513684305 4.9548331118897257497;7.0099281643715807277 -6.9348908868240055625;-2.0238084210292615062 -9.4662389175460397439;6.2970956147071417419 7.2058755842996617957;7.6129741869169951229 -6.2172924061393786133;6.7024142163421522866 7.9783686027763645043;1.2910714887497189984 -9.6964328811838100819;1.5665608886669593058 -9.6834999159671593105;-7.1299132635559887561 6.8052073972230804344;-7.748016784590799233 6.4577448099853951291;-0.99496282900153409923 9.7724391080657486697;-9.6275581985052802736 -2.3103636104707829801;7.590330063450690723 -6.3129021384257688609;9.6101038560857237059 -1.9736294318339824283;-9.6773894420689696716 1.7413017348013322128;0.15330589189411444884 9.8676708805845123607;9.6420966301760397954 - 235 2.2333650839644949571;2.2651461825737397859 9.3721733958814077425;0.60951550610306781675 9.6782709227604382107;-8.4885530533912945117 4.9442844004180654949;-9.167477080364820452 -0.4256310016695453724;4.7981883129039157865 8.8177441920918493423;6.1845773151307659177 -7.7091349620245868124;8.9231755699240462576 -4.5321793224365860553;6.4321526269651148056 -7.5911145889922817531;-9.0073179787785360162 4.7750710248460492835;1.2515795536750884409 -9.6324635920663777;0.58682496128662631207 -9.6731436539196788971;2.5154829601294395047 9.5099188217077710306;-0.19339704771597687749 9.9526627441154360554;-6.9577191428106628379 -6.8517837593023340759]; % Layer 2 b2 = [-0.38445305934767420863;-0.2995445119195868644;-0.53057559458449898848;-0.64118718988250655855;-1.0719573087662017663]; LW2_1 = [0.7474523764615736976 0.439876880725275754 -0.038006850236208339355 -0.050078889033584768342 0.0052365767121174966459 -0.050627772720815339413 -0.31447125142390236485 0.25214115850624574389 -0.96402590573760638115 -0.79533351605170432563 0.049052762180224473987 0.32759533541890301089 -0.027046485343165248272 0.01440998106478065012 -0.10795292933112216049 -0.33986934291211617909 -0.36546810265404100493 -0.63451539408605373005 -0.21352812970871937193 -0.093665739112974183422 0.30257817342191517529 0.27873140279885744164 -0.61069979529619644598 -0.44218634289041325314 0.52494656754805046894 -0.48625538977832427623 0.30143115503040829317 -0.7707256797957217076 0.6642087983665518891 -0.096797243381519293415 0.16888691806984917898 0.177721709239123421 -0.38784779089455234669 -1.0801320961751672556 0.58701680518791166108 -0.59222520444008497353 -0.26433355543515685282 -0.101743612658247648 -0.10748180973036222663 0.18614562223047764755 0.14813556839851649283 0.30890935686180365227 -0.37277441439513764321 -0.15622721034682290897 -0.26392966591939626131 -0.58613230943007454954 0.5239182120793743902 -0.72210463603000873967 1.1691896767588265682 -0.092346651863474224498;0.63563340286777669785 1.0487269994942500784 0.0040430604772041557615 -0.31181709700423848597 0.44193709420177473834 0.13335424606441406703 0.18899724832130621044 0.25357239897941019624 -0.62463013988182181091 -0.50066750805345794362 -0.072592525258437720637 0.32494010916004190115 0.19204473464459398402 0.12137929840587007024 -0.10387815593142347981 -0.2509092742752508931 -0.46448412191500110247 -0.61997530389385602234 -0.071104498386408004906 -0.16371420022460525523 0.10621045240423337974 0.24478771795244513854 -0.091833186632342037048 -0.40281728767175462025 0.07281679511960097162 0.19901821833429422792 -0.32034938224029096965 -0.89406334510153562256 0.89624143184250060212 -0.15540121132341544463 -0.05083982039333237446 0.12090441857186563779 0.33019824005259201805 0.29510258653625021097 0.50514004313274729618 0.17506214484898394157 -0.33297491602842205216 -0.13817020820487371791 0.25404859872363305673 0.094362166898263849513 0.12042533309402336794 0.16926349019736508517 -0.034927516120397759936 -0.2057428465124228889 -0.15623995820588573724 -0.20636624511130399751 -0.2209644422793363705 -0.91944697562432242677 1.1248988175960643598 -0.35580130722325403747;-0.010144171103345783758 0.16746998095312679022 0.053771178820894806327 0.15209189563851738258 0.80282434397734891984 0.49310416904236475943 0.47943984359784919125 0.12663266179433718683 -0.035797261671151589524 0.09848556804028191225 -0.032330779776195597841 0.44997957682322919926 0.1047662705050557419 0.33448025880815879241 -0.035364399153447426127 -0.18850697386207462825 -1.1676219645220953236 -0.37309636399595685896 0.75493579814915601212 -0.1346524644705069107 0.08021320037555583482 0.2404027450487261075 0.046899220936708417573 -0.12496518834106001838 0.024402224844203925652 -0.08651270610422188978 -0.034431886893732749355 -0.098435413244368094587 0.098095183218141354664 -0.17420214278684359455 -0.022258349510014860395 -0.069457671843251786448 0.19612158523429690238 0.13911928087954983368 0.47115121735998144459 0.055751981153775352629 -0.2529641171120556864 -0.21416484690855486228 0.32517966321686175846 -0.062726263463214360083 0.10075679131407351552 0.049951854604710028973 0.52445660025400031579 0.037353257945966909792 0.46053920354904864798 0.4670090785086788987 -1.0606150240765317427 -0.0033960385160934913884 0.19551378537713962058 0.10606146039749812793;0.093000461796268585335 0.40422904148524696488 0.025525617495627581088 -0.00038148817527820094719 0.74592401380084583096 0.6093987314244471154 0.47721703603312615183 0.0078202438866519299876 0.14235059468016431072 0.23881333714019734038 -0.031422103921653135594 0.39458191596507286025 -0.025686015674079559074 0.46057144214847844932 -0.029654317927137512079 -0.18373620655361155851 0.12532501283214014798 0.58513160911591710622 0.36924984192774334968 -0.15751662232146770859 0.040529815357423011613 0.41199564196026972063 -0.18773167371943766391 -0.098716918536264630646 0.26571668934403092077 -0.083555485403179016735 -0.017551489383964558044 0.20137717754117823166 -0.30279730994760412077 -0.031619620722583591244 0.006638969769555551001 -0.24997655311045588467 0.25206747713567367875 0.32726303951149848803 0.39900550478965129964 0.14591520044921896249 0.015090648249346402809 -0.34192812437515157642 0.07401268426186298488 -0.097101356635773183879 -0.053751545624486182784 0.42797488679741418904 0.27734647204434020873 -0.47946575755444592604 0.30365942404256496268 0.3061774970098586901 -0.84520412209567585826 0.048793083923721419781 0.1859718499455418339 0.14211540230493727166;-0.36986135243421813801 0.62568999243610690097 0.050992998180257195773 0.029581140336122000584 0.84020970895762014941 0.50407367070374697882 0.70754875878444534187 -0.085982744535563343513 0.096114987012256486443 0.12100394535397719642 -0.0089661312252659712552 0.35515746187255048394 0.0036090398469864667236 0.48434129319411500791 -0.0094053283583826856329 0.048631873799363081745 -0.1656245790467023582 -0.23853218758810346145 -0.12506212424152909901 -0.094189902247619430864 -0.3683202379801545634 0.28970500191975645521 0.025335311605764535003 0.18747273437276773533 0.10843074883309189449 0.8658892178869004308 -0.95589018215454601979 0.069923669699393911636 -0.061306431577331323146 -0.10199420870249953364 -0.036863653433653401081 -0.088707655830524201335 0.14916450711621803693 0.085443144218160044234 0.28450480696329161212 -0.011739275903123111422 0.11086492938907797912 -0.48800145361350666384 0.18438730367990804893 -0.085441313732557297445 0.039547376655401478285 0.22979623993930717307 0.34864696955658502908 -0.1772890817102996952 0.33628479589708931696 -0.17022567990790837822 -0.65420217622324072781 0.4444977486514839482 -0.33265785879866632824 0.19212302618293244927]; % Output 1 y1_step1.ymin = -1; y1_step1.gain = [0.333333333333333;0.333333333333333;0.5;0.5;0.5]; y1_step1.xoffset = [1;1;1;1;1]; % ===== SIMULATION ======== % Format Input Arguments isCellX = iscell(X); if ~isCellX X = {X}; 236 end % Dimensions TS = size(X,2); % timesteps if ~isempty(X) Q = size(X{1},2); % samples/series else Q = 0; end % Allocate Outputs Y = cell(1,TS); % Time loop for ts=1:TS % Input 1 Xp1 = mapminmax_apply(X{1,ts},x1_step1); % Layer 1 a1 = tansig_apply(repmat(b1,1,Q) + IW1_1*Xp1); % Layer 2 a2 = repmat(b2,1,Q) + LW2_1*a1; % Output 1 Y{1,ts} = mapminmax_reverse(a2,y1_step1); end % Final Delay States Xf = cell(1,0); Af = cell(2,0); % Format Output Arguments if ~isCellX Y = cell2mat(Y); end end % ===== MODULE FUNCTIONS ======== % Map Minimum and Maximum Input Processing Function function y = mapminmax_apply(x,settings) y = bsxfun(@minus,x,settings.xoffset); y = bsxfun(@times,y,settings.gain); 237 y = bsxfun(@plus,y,settings.ymin); end % Sigmoid Symmetric Transfer Function function a = tansig_apply(n,~) a = 2 ./ (1 + exp(-2*n)) - 1; end % Map Minimum and Maximum Output Reverse-Processing Function function x = mapminmax_reverse(y,settings) x = bsxfun(@minus,y,settings.ymin); x = bsxfun(@rdivide,x,settings.gain); x = bsxfun(@plus,x,settings.xoffset); end 10.5. Appendix of Chapter 7 Table A-7-1: Details of the seven predictors, safety strategies Predictors INITIAL DETECTION Code MANUAL FIRE PROTECTION FACILITIES Code Smoke alarm device 1 Extinguishers and standpipe system 1 Smoke detector device 2 Extinguishers 2 Heat alarm device 3 Standpipe system 3 Heat detector device 4 No manual fire protection 4 ACTION TAKEN Code SPRINKLER PROTECTION Extinguished by occupant 1 Complete sprinkler protection - supervised or watchman service 1 Burned out - no extinguishment attempted 2 Complete sprinkler protection - alarm to fire department 2 Extinguished by fire department 3 Complete sprinkler protection - unsupervised, local alarms only 3 Extinguished by an automatic system 4 Partial sprinkler protection - supervised or watchman service 4 Partial sprinkler protection - alarm to fire department 5 METHOD OF FIRE CONTROL AND EXTINGUISHMENT GROUP Code Partial sprinkler protection - unsupervised, local alarms only 6 Handheld extinguisher 1 FIXED SYSTEM OTHER THAN SPRINKLERS Standpipe & hose systems (building) 2 Supervised or watchman service 1 Makeshift firefighting aids 3 Alarm to fire departments 2 Fire department - water 4 Unsupervised, local alarms only 3 Fire department – other than water 5 No fixed system 4 Sprinkler system 6 Dry chemical system 5 Fixed system other than sprinklers 7 TRANSMISSION OF ALARM TO FIRE DEPARTMENT Code Coded signal municipal fire alarm system (included are telegraph or telephone systems, radio systems, and auxiliary connection to them) 1 Private fire alarm system (included are signals received from central stations and remote stations) 2 Radio 3 Direct verbal report to a fire station 4 238 • Advanced script using BR algorithm for fire impacts prediction % Solve an Input-Output Fitting problem with a Neural Network % This script assumes these variables are defined: % Inputs5 - input data. % Targets - target data. x = Inputs5; t = Targets; % Choose a Training Function % For a list of all training functions type: help nntrain % 'trainlm' is usually fastest. % 'trainbr' takes longer but may be better for challenging problems. % 'trainscg' uses less memory. Suitable in low memory situations. trainFcn = 'trainbr'; % Bayesian Regularization backpropagation. % Create a Fitting Network hiddenLayerSize = 10; net = fitnet(hiddenLayerSize,trainFcn); % Choose Input and Output Pre/Post-Processing Functions % For a list of all processing functions type: help nnprocess net.input.processFcns = {'removeconstantrows','mapminmax'}; net.output.processFcns = {'removeconstantrows','mapminmax'}; % Setup Division of Data for Training, Validation, Testing % For a list of all data division functions type: help nndivision net.divideFcn = 'dividerand'; % Divide data randomly net.divideMode = 'sample'; % Divide up every sample net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; % Choose a Performance Function % For a list of all performance functions type: help nnperformance net.performFcn = 'mse'; % Mean Squared Error % Choose Plot Functions % For a list of all plot functions type: help nnplot net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ... 'plotregression', 'plotfit'}; % Train the Network [net,tr] = train(net,x,t); % Test the Network y = net(x); e = gsubtract(t,y); performance = perform(net,t,y) % Recalculate Training, Validation and Test Performance trainTargets = t .* tr.trainMask{1}; valTargets = t .* tr.valMask{1}; testTargets = t .* tr.testMask{1}; trainPerformance = perform(net,trainTargets,y) valPerformance = perform(net,valTargets,y) testPerformance = perform(net,testTargets,y) % View the Network view(net) 239 % Plots % Uncomment these lines to enable various plots. %figure, plotperform(tr) %figure, plottrainstate(tr) %figure, ploterrhist(e) %figure, plotregression(t,y) %figure, plotfit(net,x,t) % Deployment % Change the (false) values to (true) to enable the following code blocks. % See the help for each generation function for more information. if (false) % Generate MATLAB function for neural network for application % deployment in MATLAB scripts or with MATLAB Compiler and Builder % tools, or simply to examine the calculations your trained neural % network performs. genFunction(net,'myNeuralNetworkFunction'); y = myNeuralNetworkFunction(x); end if (false) % Generate a matrix-only MATLAB function for neural network code % generation with MATLAB Coder tools. genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes'); y = myNeuralNetworkFunction(x); end if (false) % Generate a Simulink diagram for simulation or deployment with. % Simulink Coder tools. gensim(net); end • MATLAB function using BR algorithm for fire impacts prediction function [Y,Xf,Af] = myNeuralNetworkFunction(X,~,~) %MYNEURALNETWORKFUNCTION neural network simulation function. % % [Y] = myNeuralNetworkFunction(X,~,~) takes these arguments: % % X = 1xTS cell, 1 inputs over TS timesteps % Each X{1,ts} = 5xQ matrix, input #1 at timestep ts. % % and returns: % Y = 1xTS cell of 1 outputs over TS timesteps. % Each Y{1,ts} = 6xQ matrix, output #1 at timestep ts. % % where Q is number of samples (or series) and TS is the number of timesteps. %#ok<*RPMT0> % ===== NEURAL NETWORK CONSTANTS ===== % Input 1 x1_step1.xoffset = [1;0;0;0;1]; x1_step1.gain = [0.25;0.0202020202020202;0.222222222222222;0.222222222222222;0.25]; x1_step1.ymin = -1; % Layer 1 240 b1 = [-1.3605629297175749759;-0.015364026135297848691;-3.7252400698556131786;-2.0942498450303141944;1.89602191379389029;-1.9632984105822315346;1.8704381089059967103;0.72811106232323929977;0.34746623128488735022;-2.1778392110668387538]; IW1_1 = [0.2257591338984328011 -2.550024774760948798 0.66169800156938296531 2.2935890613454854581 -1.8327606344959102014;-2.5418877743590404528 -1.9650256208920717782 -1.1025563544774128477 1.5857452373492364472 -0.69779267554195834045;-0.17976333064369501291 -3.0586166982678251181 -1.2770381824815322247 -3.6809569705147278107 2.722825608056845148;1.4267979702674782327 -3.2053397433460268928 0.38805987624878124365 2.4144017776576940371 -0.064280645989757648207;1.9225386147364558198 1.4218624590245863004 0.28864624984941983366 2.068620938408570975 -0.13153738722406271844;-0.57904610181385030021 -0.86952855085557945181 1.2648144527203037946 0.72400089955172919343 0.71403211668776955801;2.0630795588970043397 0.51884772014082536362 0.36452359887731189403 -0.87920616426878617222 -1.3626925809408261259;0.96041279749240060504 2.0124891877187733868 0.18452648052410242085 -0.29147810083913799906 1.3426583228761850286;-0.45340121182668879918 1.5489030504583463088 0.61850497421402350628 -1.7476239513472842102 -0.3891088847283502572;-0.87687020322423891994 -1.9025206837427151019 2.5814177680603087595 -1.1702232577984525985 0.088860055168528837011]; % Layer 2 b2 = [0.76034725845028972024;-0.49710573849546213498;-0.22015847923483214399;-0.11221178529126334122;-0.3150326263421853179;-0.34407235939926456014]; LW2_1 = [-3.5686845368022006575 -1.3330788445067009995 -2.4526308560404701531 3.9385790622465113309 -3.8856733007555104287 2.1875716580847393011 1.5129879169392290716 -1.8835467692537624096 1.1241745117179220426 -2.1528417108118103052;-0.091504377303504677577 0.067195197589375699199 -0.3272717076157409255 0.014268429920866248714 0.065916165897384693118 -0.035640605646753344138 -0.0020379480103715863319 0.092664909822267799155 -0.23150423765876745885 0.021480034345501785448;-0.11829982249441187148 0.041569103132635600029 -0.32484478030281283001 -0.015316996665701576585 0.03484386886399704758 -0.18145968834019499116 -0.091004166808767897323 0.050900872221342381707 -0.22348743934810447187 0.10684414862313500183;-0.041316624621717021326 -0.16662351584132184534 0.36251069971638943956 -0.096772954882242176455 0.033966337767178368667 0.080395993073651722538 0.13618496770747404168 -0.25281551637960081313 0.065456008354851977216 -0.11985457249163983251;0.029415695061538980892 0.063048053775260251563 -0.17028715203158775737 0.086702652167190655264 0.065895513590331231124 0.043634779434291538769 -0.035027455171118770938 0.16023317830464778311 -0.11096820363832006484 -0.14106181765087846824;0.020457259671200364592 0.048605723772269529559 -0.20209988127654815449 0.074375124520236798875 0.055054960582623435617 0.05054300705280921624 -0.054646704608863089225 0.15319725238972092818 -0.13801694349017204488 -0.14262228820778644067]; % Output 1 y1_step1.ymin = -1; y1_step1.gain = [0.0202020202020202;0.333333333333333;0.285714285714286;0.5;0.666666666666667;0.666666666666667]; y1_step1.xoffset = [0;1;0;1;1;1]; % ===== SIMULATION ======== % Format Input Arguments isCellX = iscell(X); if ~isCellX X = {X}; end % Dimensions 241 TS = size(X,2); % timesteps if ~isempty(X) Q = size(X{1},2); % samples/series else Q = 0; end % Allocate Outputs Y = cell(1,TS); % Time loop for ts=1:TS % Input 1 Xp1 = mapminmax_apply(X{1,ts},x1_step1); % Layer 1 a1 = tansig_apply(repmat(b1,1,Q) + IW1_1*Xp1); % Layer 2 a2 = repmat(b2,1,Q) + LW2_1*a1; % Output 1 Y{1,ts} = mapminmax_reverse(a2,y1_step1); end % Final Delay States Xf = cell(1,0); Af = cell(2,0); % Format Output Arguments if ~isCellX Y = cell2mat(Y); end end % ===== MODULE FUNCTIONS ======== % Map Minimum and Maximum Input Processing Function function y = mapminmax_apply(x,settings) y = bsxfun(@minus,x,settings.xoffset); y = bsxfun(@times,y,settings.gain); y = bsxfun(@plus,y,settings.ymin); end % Sigmoid Symmetric Transfer Function function a = tansig_apply(n,~) a = 2 ./ (1 + exp(-2*n)) - 1; end % Map Minimum and Maximum Output Reverse-Processing Function function x = mapminmax_reverse(y,settings) x = bsxfun(@minus,y,settings.ymin); x = bsxfun(@rdivide,x,settings.gain); x = bsxfun(@plus,x,settings.xoffset); end
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Sadiq, Rehan
An integrated fire risk management of multi-unit residential buildings with smart and green features : machine learning-based framework
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