Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Woldesellasse, Haile
Doctor of Philosophy - PhD
University of British Columbia
This dissertation proposes a comprehensive decision support framework for risk assessment of oil and gas pipelines exposed to external pitting corrosion. The study begins by performing a bibliometric analysis to identify the top cited research publications on this area. After that, several imputation and class imbalance handling techniques are proposed to handle the missing values and class imbalance that are frequently observed problems in a corrosion dataset. The usefulness of both proposed techniques was assessed using a neural network model. The model's prediction performance improved when it was trained using the cleaned corrosion dataset. Then, the risk assessment framework is developed using the corrosion dataset in a bow-tie approach, which represents an entire accident scenario starting with causes and ending with consequences. The proposed framework was developed by integrating Bayesian belief network (BBN) with a geographic information system (GIS). The framework part for evaluating the failure probability of buried pipelines is developed using three basic components: corrosion model, failure pressure model, and limit state function to estimate the probability of failure due to burst and leak. Lastly, the framework part for evaluating the consequence assessment is developed using an existing event tree and additional factors including failure probability of a pipeline, igniting probability, population density, and the presence of protected or conserved areas. The consequence of gas pipeline failure are assessed based on casualty and environmental impact. The reasoning power of the BBN and the spatial capability of the GIS combine to form a powerful tool to aid decision makers manage a spatially distributed aging pipeline. Additionally, it aids pipeline operators in making timely, and cost-effective decisions to avoid accidents in high consequence areas.
Development of a spatial decisionsupport framework for risk assessment ofoil and gas pipelines exposed to externalpitting corrosionbyHaile WoldesellasseM.Sc., Khalifa University, 2017B.Sc., Eritrea Institute of Technology, 2013A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE COLLEGE OF GRADUATE STUDIES(Civil Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)April 2023© Haile Woldesellasse, 2023The following individuals certify that they have read, and recommend tothe College of Graduate Studies for acceptance, a dissertation entitled:Development of a spatial decision support framework forrisk assessment of oil and gas pipelines exposed to externalpitting corrosionsubmitted by Haile Woldesellasse in partial fulfilment of the require-ments of the degree of Doctor of PhilosophyDr. Solomon Tesfamariam, School of EngineeringSupervisorDr. Sumi Siddiqua, School of EngineeringSupervisory Committee MemberDr. Ahmad Al-Dabbagh, School of EngineeringSupervisory Committee MemberDr. Anas Chaaban, School of EngineeringUniversity ExaminerDr. Samer Adeeb, University of AlbertaExternal ExamineriiAbstractThis dissertation proposes a comprehensive decision support frameworkfor risk assessment of oil and gas pipelines exposed to external pitting corro-sion. The study begins by performing a bibliometric analysis to identify thetop cited research publications on this area. After that, several imputationand class imbalance handling techniques are proposed to handle the missingvalues and class imbalance that are frequently observed problems in a corro-sion dataset. The usefulness of both proposed techniques was assessed usinga neural network model. The model’s prediction performance improved whenit was trained using the cleaned corrosion dataset. Then, the risk assessmentframework is developed using the corrosion dataset in a bow-tie approach,which represents an entire accident scenario starting with causes and endingwith consequences. The proposed framework was developed by integratingBayesian belief network (BBN) with a geographic information system (GIS).The framework part for evaluating the failure probability of buried pipelinesis developed using three basic components: corrosion model, failure pressuremodel, and limit state function to estimate the probability of failure due toburst and leak. Lastly, the framework part for evaluating the consequenceassessment is developed using an existing event tree and additional factorsincluding failure probability of a pipeline, igniting probability, populationdensity, and the presence of protected or conserved areas. The consequenceof gas pipeline failure are assessed based on casualty and environmental im-pact. The reasoning power of the BBN and the spatial capability of the GIScombine to form a powerful tool to aid decision makers manage a spatiallydistributed aging pipeline. Additionally, it aids pipeline operators in makingtimely, and cost-effective decisions to avoid accidents in high consequenceareas.iiiLay SummaryBuried pipelines subjected to external corrosion have the potential toburst or leak, which would be harmful to society and the environment. Inthis thesis, the author developed a Bayesian belief network and GIS inte-grated decision support framework for external corrosion risk assessment ofoil and gas transmission pipelines. The study begins by handling the issuesof missing values and class imbalance in a corrosion dataset to develop therisk assessment model. The failure and consequence components of the riskassessment model are then constructed using a bow-tie method. To visualizeand prioritize inspections, the failure and consequence state of the pipes areprojected on GIS maps. The proposed spatial framework will be useful tohelp pipeline operators manage a spatially distributed aging pipeline. Theresults of the study will be crucial in helping pipeline operators avoid fail-ure and reduce the consequences of corrosion failure that can impact highconsequence areas.ivPrefaceI, Haile Woldesellasse, prepared all the contents of this thesis includingliterature review, accessing data, model development and analysis, inter-pretation of results and writing of the manuscripts under the supervisionof Dr. Solomon Tesfamariam. Dr. Solomon Tesfamariam reviewed allthe manuscripts and provided critical feedback in the improvement of themanuscripts and thesis. Most of the contents of this thesis are published,accepted or submitted for publication in the following journals.- A version of Chapter 3 has been submitted in Journal of InfrastructureIntelligence and Resilience with a title “Risk analysis of onshore oil andgas pipelines: Bibliometric analysis and literature review” (Woldesel-lasse and Tesfamariam, under review).- A version of Chapter 4 has been published in ASCE-Journal of PipelineSystems Engineering and Practice with a title “Handling Incompleteand Missing Data in Corrosion Pit Measurement Database Using Impu-tation Methods: Model Development Using Artificial Neural Network”(Woldesellasse and Tesfamariam, 2021).- A version of Chapter 5 has been published in Journal of Pipeline Sci-ence and Engineering with a title “Data Augmentation Using condi-tional Generative Adversarial Network (cGAN): Application for Pre-diction of Corrosion Pit Depth and Testing Using Neural Network”(Woldesellasse and Tesfamariam, 2022).- A version of Chapter 6 has been submitted in International Journal ofPressure Vessels and Piping with a title “Failure assessment of oil andgas pipelines using an integrated BBN-GIS model” (Woldesellasse andTesfamariam, under review).- A version of Chapter 7 has been submitted in Journal of Reliability En-gineering & System Safety with a title “Consequence assessment of gaspipeline using an integrated Bayesian belief network and GIS model:vApplication with Alberta pipeline” (Woldesellasse and Tesfamariam,under review).viTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiList of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . xviiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . xixDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxChapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . 11.1 Background and motivation . . . . . . . . . . . . . . . . . . . 11.2 Research objective . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Thesis organization . . . . . . . . . . . . . . . . . . . . . . . . 4Chapter 2: Literature review . . . . . . . . . . . . . . . . . . . . 62.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Soil factors influencing external corrosion . . . . . . . . . . . 72.2.1 Soil texture . . . . . . . . . . . . . . . . . . . . . . . . 82.2.2 Presence of water . . . . . . . . . . . . . . . . . . . . . 82.2.3 Redox potential . . . . . . . . . . . . . . . . . . . . . . 82.2.4 pH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.5 Resistivity . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.6 Ions content . . . . . . . . . . . . . . . . . . . . . . . . 9viiTABLE OF CONTENTS2.3 Handling corrosion database . . . . . . . . . . . . . . . . . . . 102.3.1 Missing values . . . . . . . . . . . . . . . . . . . . . . 102.3.2 Imbalanced dataset . . . . . . . . . . . . . . . . . . . . 112.4 Corrosion prediction models . . . . . . . . . . . . . . . . . . . 162.5 Risk assessment . . . . . . . . . . . . . . . . . . . . . . . . . . 192.5.1 Probability of failure . . . . . . . . . . . . . . . . . . . 242.5.2 Consequence of failure . . . . . . . . . . . . . . . . . . 27Chapter 3: Bibliometric analysis for risk assessment of on-shore oil and gas pipelines . . . . . . . . . . . . . . 313.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2 Biblometric analysis . . . . . . . . . . . . . . . . . . . . . . . 313.3 Research methods . . . . . . . . . . . . . . . . . . . . . . . . 323.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . 343.4.1 Trend of research . . . . . . . . . . . . . . . . . . . . . 343.4.2 Bibliographic coupling . . . . . . . . . . . . . . . . . . 363.4.3 Co-occurrence analysis (co-word analysis) . . . . . . . 373.4.4 Network analysis . . . . . . . . . . . . . . . . . . . . . 393.4.5 Citation analysis . . . . . . . . . . . . . . . . . . . . . 403.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Chapter 4: Handling incomplete and missing data in corro-sion pit measurement database using imputationmethods . . . . . . . . . . . . . . . . . . . . . . . . . 544.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.2 Data description . . . . . . . . . . . . . . . . . . . . . . . . . 544.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 574.3.1 Imputation methods . . . . . . . . . . . . . . . . . . . 594.3.2 Neural network (NN) . . . . . . . . . . . . . . . . . . . 654.3.3 Performance evaluation . . . . . . . . . . . . . . . . . 664.4 Result and discussion . . . . . . . . . . . . . . . . . . . . . . . 674.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Chapter 5: Handling class imbalance in a corrosion pit mea-surement database . . . . . . . . . . . . . . . . . . . 765.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765.2 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . 765.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 785.3.1 Borderline-SMOTE . . . . . . . . . . . . . . . . . . . . 805.3.2 Generative Adversarial Network (GAN) . . . . . . . . 81viiiTABLE OF CONTENTS5.3.3 Artificial Neural Network . . . . . . . . . . . . . . . . 835.3.4 Performance evaluation . . . . . . . . . . . . . . . . . 845.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . 845.4.1 Results for data augmentation using ROS, SMOTEand cGAN . . . . . . . . . . . . . . . . . . . . . . . . . 855.4.2 Result for pit depth prediction using ANN . . . . . . . 865.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Chapter 6: Failure assessment of oil and gas pipelines usingan integrated BBN-GIS model . . . . . . . . . . . . 936.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 936.2 Spatial Bayesian network . . . . . . . . . . . . . . . . . . . . 936.2.1 Bayesian belief network . . . . . . . . . . . . . . . . . 936.2.2 Integrating BBN with GIS . . . . . . . . . . . . . . . . 966.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 966.3.1 BBN model development for external corrosion . . . . 986.3.2 Case study: Province of Alberta’s oil and gas pipelinenetwork . . . . . . . . . . . . . . . . . . . . . . . . . . 1106.4 Result and discussion . . . . . . . . . . . . . . . . . . . . . . . 1146.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124Chapter 7: Consequence assessment of gas pipeline failure us-ing an integrated Bayesian network and GIS . . . 1277.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1277.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 1277.2.1 Event tree . . . . . . . . . . . . . . . . . . . . . . . . . 1297.2.2 Social impact . . . . . . . . . . . . . . . . . . . . . . . 1307.2.3 Environmental impact . . . . . . . . . . . . . . . . . . 1407.3 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1457.4 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . 1467.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Chapter 8: Conclusions and recommendations . . . . . . . . . 1598.1 Summary and conclusions . . . . . . . . . . . . . . . . . . . . 1598.2 Originality and contributions . . . . . . . . . . . . . . . . . . 1618.3 Limitations and recommendations . . . . . . . . . . . . . . . . 163Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165Appendix A: Example code for imputation techniques . . . . . . . 188ixList of TablesTable 1.1 PHMSA all reported pipeline incidents (2002-2021). . . 2Table 2.1 Relationship between soil parameters and corrosion,taken from Arriba-Rodriguez et al. (2018). . . . . . . . 7Table 2.2 Summary of soil and pipe factors affecting the corro-sion of oil and gas pipelines. . . . . . . . . . . . . . . . 18Table 3.1 Search strings used to collect information on publica-tions related to risk assessment of oil and gas pipelines. 34Table 3.2 Bibliographic coupling of contributing authors, coun-tries, and organizations. . . . . . . . . . . . . . . . . . 36Table 3.3 TLS for author and index keywords. . . . . . . . . . . 39Table 3.4 The most active authors, organizations and countriesthat have largest number of articles focusing on riskassessment of oil and gas pipelines. . . . . . . . . . . . 42Table 3.5 Citation analysis for top 20 authors, organizations, andcountries. . . . . . . . . . . . . . . . . . . . . . . . . . 43Table 6 The top 20 articles with the most citation. . . . . . . . 44Table 3.7 Journal ranking according to citation analysis. . . . . . 51Table 4.1 Statistical summary of the variables with missing data. 58Table 4.2 Performance of the imputation techniques on the testdataset. . . . . . . . . . . . . . . . . . . . . . . . . . . 70Table 4.3 Statistics of the data used in the NN model. . . . . . . 71Table 4.4 Model performance on the datasets created by the dif-ferent imputation techniques. . . . . . . . . . . . . . . 73Table 5.1 Statistics of the data used in the NN model. . . . . . . 85Table 5.2 Architecture of both generator and discriminator. . . . 86Table 5.3 ANN model performance on the training and testingdataset. . . . . . . . . . . . . . . . . . . . . . . . . . . 90xLIST OF TABLESTable 6.1 Regression coefficients by soil types (Velázquez et al.,2009). . . . . . . . . . . . . . . . . . . . . . . . . . . . 100Table 2 Discretized nodes of the BBN corrosion model. . . . . 102Table 6.3 Discretized nodes of the BBN burst pressure model. . . 108Table 6.4 Discretized nodes of the failure probability BBN model. 110Table 6.5 Pipeline characteristics for assessing failure probability. 119Table 7.1 Equations used to estimate the ignition probability. . . 132Table 7.2 Equations used to estimate expected number of fatali-ties resulting from exposure to heat emitted from a gasfire. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133Table 3 Discretized nodes of the BBN casualty model. . . . . . 137Table 4 Discretized nodes of the BBN model for environmentalimpact assessment. . . . . . . . . . . . . . . . . . . . . 142Table 7.5 Scenario analysis of the proposed BBN-based casualtyestimation framework. . . . . . . . . . . . . . . . . . . 148Table 7.6 Analysis of the proposed BBN-based casualty estima-tion for burst and leak failure probabilities. . . . . . . 153Table 7.7 Scenario analysis of the proposed BBN-based environ-mental impact framework. . . . . . . . . . . . . . . . . 155xiList of FiguresFigure 1.1 Causes of pipeline incidents from 2005 - present (Source:US DOT Pipeline and Hazardous Materials SafetyAdministration). . . . . . . . . . . . . . . . . . . . . . 3Figure 1.2 Thesis organization. . . . . . . . . . . . . . . . . . . . 5Figure 2.1 Schematic view of risk analysis components. . . . . . 20Figure 2.2 Structure of a bow-tie model. . . . . . . . . . . . . . . 24Figure 3.1 Research design for bibliometric analysis of retrievedpapers. . . . . . . . . . . . . . . . . . . . . . . . . . . 32Figure 3.2 Number of articles published per year. . . . . . . . . . 35Figure 3.3 (a) Network and (b) density diagrams for author key-words. Note: Threshold criteria of minimum 5 key-words. . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Figure 3.4 Co-occurence map of authors keywords according toyears. . . . . . . . . . . . . . . . . . . . . . . . . . . . 40Figure 3.5 Co-authorship analysis of authors. Note: Thresholdcriteria—minimum of three documents and thirty ci-tations; resolution—1.0; 196 of 4013 authors are con-nected to each other. . . . . . . . . . . . . . . . . . . 41Figure 3.6 Co-authorship analysis according to organizations. Note:resolution—1.0. . . . . . . . . . . . . . . . . . . . . . 42Figure 3.7 Co-authorship analysis according to country. Note:Threshold criteria of minimum of three documentsand thirty citations, resolution of 1.0. . . . . . . . . . 43Figure 3.8 Citation analysis according to sources. Note: Thresh-old criteria of minimum of three documents and thirtycitations, resolution of 1.0. . . . . . . . . . . . . . . . 52Figure 4.1 Plot of the measured soil samples overlaid on the ternarydiagram. . . . . . . . . . . . . . . . . . . . . . . . . . 55xiiLIST OF FIGURESFigure 4.2 Pairwise correlation of the continuous variables in theNBS data. . . . . . . . . . . . . . . . . . . . . . . . . 56Figure 4.3 Corrosion datasets with their observed (grey) and miss-ing values (red) . . . . . . . . . . . . . . . . . . . . . 57Figure 4.4 Boxplot of soil variables that have missing values. . . 58Figure 4.5 Flowchart of proposed methodology. . . . . . . . . . . 59Figure 4.6 Comparison of observed and imputed values of sand,silt, clay, colloid, suspension, and specific gravity. Thesymbols red cross, black diamond, blue cross, and greecircle represent mean, median, LR, and KNN mputa-tion methods. . . . . . . . . . . . . . . . . . . . . . . 68Figure 4.7 Comparison of observed and imputed values of sand,silt, clay, colloid, suspension, and specific gravity. Thesymbols red cross, black diamond, blue cross, and greecircle represent IRMI, IMPSEQ, AMELIA, and PCAimputation methods. . . . . . . . . . . . . . . . . . . 69Figure 4.8 Comparison of the observed and predicted values ofthe testing set (Baseline Model). The red line indi-cates line of best fit. . . . . . . . . . . . . . . . . . . . 72Figure 4.9 Comparison of the observed and predicted pit depthof the testing set. The red line indicates line of best fit. 73Figure 4.10 Relative contribution of the corrosion factors. . . . . . 74Figure 5.1 Number of observations for the different soil types. . . 77Figure 5.2 Pairwise correlation of the corrosion variables. . . . . 78Figure 5.3 Pairwise scatter plot of the corrosion variables. . . . . 79Figure 5.4 Work flow of the proposed study. . . . . . . . . . . . . 80Figure 5.5 Structure of (a) GAN and (b) cGAN. . . . . . . . . . 81Figure 5.6 Structure of the neural network. . . . . . . . . . . . . 84Figure 5.7 Histogram comparison between original and augmenteddata by random oversampling (red color indicates theoriginal data and blue color indicates the augmenteddata). . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Figure 5.8 Histogram comparison between original and augmenteddata by Borderline-SMOTE (red color indicates theoriginal data and blue color indicates the augmenteddata). . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Figure 5.9 Histogram comparison between original and augmenteddata by cGAN (red color indicates the original dataand blue color indicates the augmented data). . . . . 89xiiiLIST OF FIGURESFigure 5.10 Comparison of observed and predicted corrosion pitdepth. The figures are obtained from the ANN trainedwith the original dataset: (a) Training accuracy and(b) Testing accuracy. . . . . . . . . . . . . . . . . . . 90Figure 5.11 Comparison of observed and predicted corrosion pitdepth. The figures are obtained from the ANN trainedwith the cGAN based augmented dataset: (a) Train-ing accuracy and (b) Testing accuracy. . . . . . . . . 91Figure 6.1 A schematic of BBN (modified after Cockburn andTesfamariam Cockburn and Tesfamariam (2012) ). . . 95Figure 6.2 Spatial Bayesian network flowchart for predicting theprobability that an oil and gas pipeline may fail dueto external pitting corrosion . . . . . . . . . . . . . . 97Figure 6.3 BBN to analyze pipeline failure probability due to ex-ternal corrosion . . . . . . . . . . . . . . . . . . . . . 99Figure 6.4 Pairwise correlation of the corrosion variables . . . . . 100Figure 6.5 Alberta’s transmission pipeline system for oil and gas. 111Figure 6.6 Pipe characteristics, including (a) Pipeline age , (b)Maximum operating pressure , (c) Pipe diameter, (d)Pipe thickness, (e) Ultimate tensile strength, (f) coat-ing type. . . . . . . . . . . . . . . . . . . . . . . . . . 112Figure 6.7 Histogram for (a) Pipeline age , (b) Maximum oper-ating pressure , (c) Pipe diameter, (d) Pipe thickness,(e) Ultimate tensile strength, (f) coating type. . . . . 113Figure 6.8 Soil characteristics (a) Soil type , (b) Bulk density ,(c) Water content, (d) Soil pH. . . . . . . . . . . . . . 115Figure 6.9 BBN model prediction for corrosion defect depth us-ing different discretization methods: (a) Equal inter-val, (b) Equal quantile and (c) Moment matching . . 116Figure 6.10 BBN model prediction for corrosion defect depth: (a)Training dataset and (b) Testing dataset . . . . . . . 117Figure 6.11 Comparison of the experimental test failure pressurewith the BBN model predicted failure pressure, ob-tained from Tesfamariam et al. Tesfamariam et al.(2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . 118Figure 6.12 Changes in the burst and leak failure probabilities fordifferent discretization techniques. . . . . . . . . . . . 119Figure 6.13 Sensitivity analysis of the BBN model for: (a) Maxi-mum pit depth and (b) Failure probabilities . . . . . . 120xivLIST OF FIGURESFigure 6.14 Fragility curves for variation in soil types: (a) Burstfailure and (b) Leakage failure . . . . . . . . . . . . . 121Figure 6.15 Fragility curves for variation in pipe coating types:(a) Burst failure and (b) Leakage failure . . . . . . . . 122Figure 6.16 Failure probability of oil and gas pipelines due to: (a)Burst and (b) Leakage . . . . . . . . . . . . . . . . . . 123Figure 6.17 Failure probability of oil and gas pipelines due to ex-ternal pitting corrosion. . . . . . . . . . . . . . . . . . 124Figure 6.18 Failure probability distribution for the pipeline seg-ments in the system. . . . . . . . . . . . . . . . . . . . 125Figure 7.1 Flowchart for estimating the consequence of gas pipelinefailure due to external pitting corrosion. . . . . . . . . 128Figure 7.2 Event tree analysis adapted to natural gas release. . . 129Figure 7.3 BBN model for calculating expected number of fatal-ities from a gas pipeline failure. . . . . . . . . . . . . 131Figure 7.4 Contour plot of the ignition probability due to: (a)Burst and (b) Leakage . . . . . . . . . . . . . . . . . . 132Figure 7.5 Illustration of societal impact integration . . . . . . . 133Figure 7.6 Expected number of fatalities due to rupture in (a)Location class 1 (24 people/km2), (b) Location class 2(132 people/km2) , (c) Location class 3 (608 people/km2),(d) Location class 4 (3000 people/km2). . . . . . . . . 134Figure 7.7 Expected number of fatalities due to leakage in (a)Location class 1 (24 people/km2), (b) Location class 2(132 people/km2) , (c) Location class 3 (608 people/km2),(d) Location class 4 (3000 people/km2). . . . . . . . . 135Figure 7.8 BBN model for environmental impact assessment . . . 140Figure 7.9 Alberta’s population density per square kilometre in2016. . . . . . . . . . . . . . . . . . . . . . . . . . . . 145Figure 7.10 Alberta’s protected and conserved areas. . . . . . . . 146Figure 7.11 Alberta’s water body, including rivers and lakes. . . . 147Figure 7.12 Sensitive parameters of the BBN model for: (a) Ca-sualty and (b) Environmental impact . . . . . . . . . 148Figure 7.13 Casualty-probability for the five states . . . . . . . . . 149Figure 7.14 BBN output for the casualty model . . . . . . . . . . 150Figure 7.15 Pipe segment classification based on casualty classes. 151Figure 7.16 Failure probability distribution for the pipeline seg-ments in the system. . . . . . . . . . . . . . . . . . . . 152xvLIST OF FIGURESFigure 7.17 BBN output for the casualty model, focusing on thecity of: (a) Calgary and (b) Edmonton . . . . . . . . 152Figure 7.18 Comparison of the observed and estimated potentialimpact radius. . . . . . . . . . . . . . . . . . . . . . . 154Figure 7.19 Environmental impact-probability for the three states 156Figure 7.20 BBN output for the environmental impact model . . . 157Figure 7.21 Pipe segment categorisation based on environmentalimpact states. . . . . . . . . . . . . . . . . . . . . . . 158xviList of AbbreviationsAER Alberta Energy RegulatorAMELIA Multiple Imputations of Incomplete Multivariate DataANN Artificial Neural NetworkBBN Bayesian Belief NetworkcGAN Conditional Generative Adversarial NetworkCPT Conditional Probability TableDAG Directed Acyclic GraphDNV Det Norske VeritasEMB Expectation-Maximization with a BootstrappingET Event TreeFP Failure PressureFT Fault TreeGIS Geographic Information SystemHCA High Consequence AreaIMPSEQ Sequential Imputation for Missing ValuesIRMI Iterative Robust Model-based ImputationKNN K-Nearest NeighourLSF Limit State FunctionML Machine LearningMLP Multilayer PerceptronxviiNACE National Association of Corrosion EngineersNBS National Bureau of StandardsOP Operating PressurePCA Principal Component AnalysisPHMSA Pipeline and Hazardous Materials Safety AdministrationPIR Potential Impact RadiusPoF Probability of FailureSMOTE Synthetic Minority Over-sampling TechniqueTLS Total link strengthUTS Ultimate Tensile StrengthWoS Web of SciencexviiiAcknowledgementsFirst and foremost, I thank the Almighty God for His unconditional love,mercy, and grace that sustained me in every step of my life.My deepest gratitude goes to my academic supervisor and mentor Dr.Solomon Tesfamariam, who has provided professional support throughoutmy PhD journey. Very much appreciate his ongoing research guidance andcritical supervision of every aspect of my dissertation. His role was crucialfrom the beginning to the end of my PhD research. I owe him my sinceregratitude for his invaluable support and for giving me the chance to growas a researcher. Also, would like to express my sincere appreciation to Dr.Sumi Siddiqua and Dr. Ahmad Al-Dabbagh, who are also members of mycommittee, for their support, as well as insightful and helpful feedback in myresearch. It was a great pleasure to work with my colleagues: Dr. YekenalemAbebe, Dr. Ngandu Balekelayi, Dr. Edel Rolando, Dr. Kostas Skandalos,Dr. Sourav Das, Oleg Shabarchin, Biniam Teweldebrhan, Ikenna Odikam-noro, Yishak Tsehay, Tongchen Han, and Madihe Khanjari. Thank you allfor the feedback and insightful discussions.This research could not have been possible without the financial supportfrom the Natural Sciences and Engineering Research Council under the Dis-covery Grant programs, Mitacs-Accelerate and partial financial support fromthe University of British Columbia as the University Graduate Fellowship.Lastly, without the support of my family and friends this journey wouldnot have been possible. I would like to thank my parents for their guidance,support and for motivating me to pursue my dreams. For their unwaver-ing support, my siblings Yosief Tecle, Sirak Tecle and Betelhem Kidanewill always have my gratitude. I am extremely fortunate to have receivedthe support and encouragement of my beloved uncle Tekhle Ghebre-Medhinthroughout my academic studies. I will always be grateful to my late uncleLeakemariam Ogbamichael for his encouragement and assistance during mystudies. Last but not least, I am forever thankful to Haben Negash, my dearwife, for her constant encouragement, patience, and support throughout myPhD research.xixDedicationTo my family with love and eternal appreciationxxChapter 1Introduction1.1 Background and motivationCompared to other modes of transportation, such as road and rail, pipelinesare considered to be the safest and most cost-effective way to move hazardousliquids and natural gas across long distances (Dey, 2004; Singh and Marke-set, 2009; Ramírez-Camacho et al., 2017). However, a pipeline failure canresult in serious personal injuries, monetary losses, and environmental harm(Yin et al., 2022; Shahriar et al., 2012; Belvederesi et al., 2018). The USPipeline and Hazardous Materials Safety Administration (PHMSA) keepstrack of information on the frequency of pipeline failures, incidents, and ac-cidents. As shown in Table 1.1, the PHMSA reports that in average thereare 640 pipeline incidents, 14 fatalities, and 57 injuries every year. Figure1.1 summarizes the causes for oil and gas pipeline failures from 2005 to Oc-tober 19, 2022 using data from the PHMSA incident database. Equipmentfailure (31.41%) and corrosion (17.57%) are the two main causes of pipelineincidents, followed by excavation damage (11.2%) and incorrect operation(10.44%). From 1990 to 2012, internal corrosion (54.8%) and external cor-rosion (12.7%) were the two main failure reasons, according to the AlbertaEnergy Regulator (AER) in Canada (AER, 2013).A corroded pipeline may fail as a result of rupture or leaks. When anoil pipeline fails, the soil, groundwater, or surface water may get contam-inated. In contrast, a natural gas pipeline rupture is more likely to causean explosion and fire, which could cause fatalities and serious injuries. Thecompanies are responsible for the costs associated with accidents, which hasa huge financial impact on the oil and gas industry. Internal corrosion can betreated and reduced by adding chemicals, while exterior corrosion can onlybe mitigated by excavating the pipeline and then rehabilitating (e.g., recoat-ing) it, which is costly (AER, 2013). Therefore, decision support systemsmust be developed to assist pipeline operators identify pipelines that aresusceptible to failure due to external corrosion and reduce the consequencesof failure that could affect a high consequence area (HCA).Pipeline infrastructure have different failure probabilities and consequences11.1. BACKGROUND AND MOTIVATIONTable 1.1: PHMSA all reported pipeline incidents (2002-2021).Calendar Year Incidents Fatalities Injuries Total Cost ($)2002 642 12 49 102,167,5882003 670 12 71 139,044,0042004 671 23 60 267,836,5022005 719 17 47 1,245,463,1892006 639 21 36 151,983,7672007 610 15 49 153,772,4322008 659 8 56 564,830,8402009 627 13 64 179,070,1832010 586 22 108 1,692,501,8872011 588 13 55 426,330,2612012 571 12 57 229,852,6642013 617 9 44 368,993,9392014 706 19 95 368,066,3502015 712 11 48 351,449,8512016 632 16 87 376,497,7252017 646 7 32 340,276,2232018 634 7 78 2,262,348,6962019 657 11 36 351,035,3002020 577 15 40 385,902,6652021 632 13 32 220,942,767Grand Total 12795 276 1144 10,178,366,83320 Year Average 640 14 57 508,918,342based on their location since certain pipe segments experience more corro-sion deterioration than others (Wang et al., 2021). Corrosion rates differ atdifferent points in a pipeline network because of the high spatial variabilityand heterogeneous characteristics of soil properties. For the risk analysisof the oil and natural gas pipelines subjected to external pitting corrosion,an integrated Bayesian belief network (BBN) and Geographic InformationSystem (GIS) technique is used in this study. The spatial decision supportframework in this case is developed using spatial data (such as pipe charac-teristics, soil information, demographic data, and others) and probabilisticgraphical models (BBN). In general, it is challenging to find a complete cor-rosion dataset, and the current public corrosion dataset has issues of missingdata and class imbalance. The class imbalance occurs when more corrosionsamples are taken from pipes buried in one soil type than another. A com-21.2. RESEARCH OBJECTIVE5.1117.5731.4111.210.448.76.688.89 ALL OTHER CAUSESCORROSIONEQUIPMENT FAILUREEXCAVATION DAMAGEINCORRECT OPERATIONMATERIAL FAILURE OF PIPE OR WELDNATURAL FORCE DAMAGEOTHER OUTSIDE FORCE DAMAGEFigure 1.1: Causes of pipeline incidents from 2005 - present (Source: USDOT Pipeline and Hazardous Materials Safety Administration).plete corrosion dataset is needed to build a risk model. GIS is widely utilizedto gather spatial data and has shown to be a potent tool for decision mak-ers by offering a dynamic platform for pipeline visualization and mapping.On the other hand, BBN excels in reasoning and decision analyses basedon limited data or expert opinions (Kanti Sen and Dutta, 2020). Althoughsome research has been done on modelling risk using the BBN approach, itmust be noted that no research has been done that integrates BBN and GISto estimate the risk of oil and gas pipeline infrastructure exposed to exter-nal corrosion and facilitate decision-making on maintaining the pipe assetagainst corrosion. The results of the study will be crucial in helping pipelineoperators prevent failure and reduce the consequences of corrosion failurethat can impact HCAs.1.2 Research objectiveThe main goal of this study is to develop a framework by integrating BBNand GIS for dynamic risk assessment of oil and natural gas transmissionpipelines exposed to external pitting corrosion. The BBN model analysesthe spatial inputs from the GIS and returns the failure and consequencelevels at the target location. The GIS platform then receives the failure and31.3. THESIS ORGANIZATIONconsequence estimates and shows them spatially. This kind of informationlinking provides a strong tool for making decisions under uncertainty. Theprecise objectives consist of:1. Develop imputation techniques to deal with missing and incompletedata in a corrosion pit measurement database.2. Develop a data augmentation approach to handle imbalanced soil sam-ples in the corrosion database using a conditional generative adversarialnetwork (cGAN).3. Develop an oil and gas pipeline failure assessment model using an in-tegrated BBN model and GIS.4. Develop an integrated BBN model and GIS for the purpose of assessingthe consequence of a gas pipeline failure.1.3 Thesis organizationThe rest of the dissertation is structured as depicted in Figure 1.2. Chap-ter 2 reviews relevant literature and goes into detail about handling corrosiondatabases and risk assessment of oil and gas pipelines. Chapter 3 presentsbibliometric analysis for risk assessment of onshore oil and gas pipelines.The development of the proposed frameworks and their mathematical for-mulation are covered in chapters 4 to 7. The conclusions, limitations, andsuggestions for further research are finally summarized in chapter 8.41.3. THESIS ORGANIZATIONChapter 1IntrodutionChapter 2Literature reviewChapter 3Bibliometric analysis for risk assessment of onshore oil and gas pipelinesChapter 4 (Objective 1)Handling incomplete and missing data in corrosion pit measurement database using imputation methodsChapter 5 (Objective 2)Handling class imbalance in a corrosion pit measurement databaseChapter 6 (Objective 3)Failure assessment of oil and gas pipelines using an integrated BBN-GIS modelChapter 7 (Objective 4)Consequence assessment of gas pipelines using an integrated BBN-GIS modelChapter 8Conclusion and recommendationsFigure 1.2: Thesis organization.5Chapter 2Literature review2.1 IntroductionCorrosion in buried pipelines is the degradation of steel caused by achemical or electrochemical reaction between a pipeline and its surroundings(Arriba-Rodriguez et al., 2018). It is a time-dependent damage mechanismthat is dependent on the soil properties around the pipeline and protectionbarriers (such coatings and cathodic protection) (Amaya-Gómez et al., 2021).When a metallic pipe comes into touch with soil that has different propertiesor due to a material difference, the corrosion process starts to take place.The portions of the pipe that are situated in the most conductive soil andthe low conductivity area of the ground serve as the anode and cathode,respectively. For the generation of a corrosion cell, the metallic pipe servesas an essential element and the soil serves as an electrolyte. As a result, acurrent travels through the soil from the anodic region to the cathodic areabefore returning via the steel tube of the cathode area to the anodic areato complete the circuit. Corrosion forms in this location as a result of themetal’s discharge of current to the ground in the anodic area. In contrast,the metallic structure in the cathodic area is kept safe by the current thatflows from the ground to it (Arriba-Rodriguez et al., 2018).When corrosion-stimulating elements in the soil locally damage, remove,or destabilize the protective oxide, or passive layer, on an uncoated pipeline’sexternal surface, corrosion can start. This causes a progressive and localizedmetal loss on the pipeline’s external surface, reducing the pipe’s wall thick-ness. The process, in combination with high operating pressures, can thensignificantly shorten the pipeline’s service life and compromise its structuralintegrity (Wasim and Djukic, 2022). Therefore, it is important for pipelineoperators to assess the risk of corrosion in buried pipes and create a proactivemitigation plan.The state of the art literature on environmental factors that affect ex-ternal corrosion, data cleaning challenges in corrosion datasets, models forpredicting corrosion defects, and a framework for evaluating pipeline failureand consequences is presented in this part. The weaknesses and limitations62.2. SOIL FACTORS INFLUENCING EXTERNAL CORROSIONof the existing literature are also identified in this section.2.2 Soil factors influencing external corrosionMetal pipes buried underground are susceptible to corrosion because soilis such a complex corrosive environment. The features of the soil in which theunderground pipes are located have a significant impact on how quickly theydegrade. The primary factors that affect corrosion of underground infras-tructures are soil texture, presence of water, aeration, redox potential, pH,resistivity, ions content, bacteria and others (Chaker, 1989; Arriba-Rodriguezet al., 2018). Since it is challenging to model external corrosion using elec-trochemical reaction, various studies have developed predictive models thatdepend on these soil factors to predict the growth of corrosion defects onburied steel pipelines (Velázquez et al., 2009). Kim et al. (2021) have cate-gorized the factors that affect corrosion into global (soil type, liquid precip-itation, soil temperature, and pipeline topography) and local (particle size,moisture saturation, resistivity, pH value, and content of inorganic speciessuch as chlorides, sulfate and bicarbonate) factors based on the scale of theparameters. In general, it is believed that soil types with greater chlorideconcentrations, an acidic pH, and the presence of bacteria or fungi have amajor impact on exterior corrosion (Amaya-Gómez et al., 2021). Table 2.1depicts the relationship between soil properties and corrosion, with the pa-rameters having either a direct or inverse relationship to the mechanism ofdegradation (Arriba-Rodriguez et al., 2018).Table 2.1: Relationship between soil parameters and corrosion, taken fromArriba-Rodriguez et al. (2018).Soil parameter RelationSoil texture DirectPresence of water DirectAeration DirectpH InverseResistivity InverseRedox potential DirectIon contents DirectBacteria Direct72.2. SOIL FACTORS INFLUENCING EXTERNAL CORROSION2.2.1 Soil textureClay, silt, and sand are the three types of soil texture, which representsthe size distribution of the mineral particles that make up the soil. Clay soilhas a finest particle size and minimum pore volume between particles. Whenit’s wet, this can result in decreased water and air flow and poor aerationconditions. Sand, on the other hand, promotes increased distribution ofmoisture and aeration and has the biggest particle size. These have animpact on the diffusion of gases and other soluble salts (Chaker, 1989). Asa result, clay soil has a larger negative corrosion potential than sandy soil,which might locally increase the rate of external corrosion of the pipelinesection in clay soil (Wasim and Djukic, 2022). Clay, clay loam, and sandyclay loam were ranked in decreasing order of corrosiveness by Velázquezet al. (2009), with the finer particles being considered the worst corrosivemedia for steel. This is mainly due to finer particles absorbs more water,and liquid water represents the essential electrolyte for the electrochemicalcorrosion reactions (Arriba-Rodriguez et al., 2018). Oguzie et al. (2004)also mentioned how the characteristics of the soil’s textural properties affectcorrosion rate. Using a ternary diagram that uses percentages of sand, silt,and clay as values, soil can be divided into various textural categories.2.2.2 Presence of waterOne of the main causes of exterior corrosion of steel pipelines is the mois-ture content of the soil, which functions as an electrolyte for the corrosionprocess. The sources of water in soil can be rainfall or snowfall, water insidethe void pores, and groundwater (Amaya-Gómez et al., 2021). The gen-eral consensus about moisture’s impact on corrosion rate is that the rate ofcorrosion for buried metal structures increases with an increase of moisturecontent until the moisture reaches a critical level. When the moisture contentincreases beyond a certain level, the corrosion rate begins to decrease. Thecritical moisture content changes depending on the type of soil, pipeline steelused, conditions surrounding metal exposure, and the duration the metal isexposed to the soil (Wasim and Djukic, 2022; Wasim et al., 2018). Addition-ally, soil resistivity is influenced by moisture content, with dry soils havinghigher resistivity can slow down corrosion rate (Amaya-Gómez et al., 2021).2.2.3 Redox potentialRedox potential tries to distinguish between aerobic and anaerobic soilsthat can support sulfate-reducing bacterial activity (Chaker, 1989). As a82.2. SOIL FACTORS INFLUENCING EXTERNAL CORROSIONresult, redox potential serves as a measure for the soil’s aeration, with a highvalue indicating a high level of oxygen. A low redox potential values sig-nify favorable conditions for anaerobic microbial activity (Arriba-Rodriguezet al., 2018).2.2.4 pHBased on the pH, soil reactivity is calculated, with values between 3.5 and9.5 (Amaya-Gómez et al., 2021). Early research on corrosion found a con-nection between pH and corrosion rate (Romanoff, 1957). But according torecent research, soil pH alone is a weak predictor of corrosion in undergroundpipelines (Wasim et al., 2018; Amaya-Gómez et al., 2021). When predictingcorrosion, King (1977) advised measuring the soil resistivity and pH becausedoing the latter without the former can give ambiguous results. Dependingon the resistivity, both acidic and alkaline soils may have a corrosion aggres-siveness (King, 1977). Arriba-Rodriguez et al. (2018) have confirmed thisfinding, which states that according to European Standard EN 12501-2:2003,both soil pH and resistivity are deemed to provide a qualitative evaluationof soil corrosivity.2.2.5 ResistivitySoil resistivity, which indicates the ability of an environment to carry cor-rosion currents, is often looked upon as indicative of a soil’s corrosivity. Someof the factors that affect soil resistivity include soil porosity, soil compaction,content of soluble ions and ground water conductivity. For example, resis-tivity decrease for soils with an increased content of ions or higher porosity(Amaya-Gómez et al., 2021). Soil type that has lower resistivity are morelikely to promote corrosion because the soil behaves as a good electrolyte(Chaker, 1989).2.2.6 Ions contentChlorides and sulfates are the two chemical components that are com-monly related to corrosion. The presence of chloride ions tends to decreasesoil resistivity, and regarded as harmful because they participate in the reac-tions of anodic dissolution of metals. The concentration of chloride ion variesdepending on the degree of soil moisture. Sulfate ions, on the other hand,are less corrosive than chlorides. However, their impact becomes alarmingwhen sulfates changes into a highly corrosive sulfides due to the activity ofbacteria (Arriba-Rodriguez et al., 2018; Li et al., 2007; Arzola et al., 2003).92.3. HANDLING CORROSION DATABASE2.3 Handling corrosion databaseCorrosion database suffers from major issues such as missing values, out-liers, or imbalance in the class distribution (Velázquez et al., 2009; Woldesel-lasse and Tesfamariam, 2021). Some of the data gathered from sites duringa corrosion data collection may be partially missing due to failure of datamonitoring and detection equipment, errors in the data storage process, andother problems. This could lead to the removal of important corrosion infor-mation (Xu et al., 2022). Moreover, the majority of the corrosion samplesin the corrosion database might have come from one soil type, and the re-maining corrosion samples might have come from one or more different soiltypes. This can result in a class imbalance. As a result, many methods forestimating missing values, removing outliers, and resolving the problem ofclass imbalance have been developed. The methods for estimating missingvalues and generating samples to solve a class imbalance problem are coveredin the following sections.2.3.1 Missing valuesMissing value is a common problem when creating statistical predictionmodels. A model’s performance will be poor if a database contains missingvalues. Data imputation is a key strategy that is used to find the most prob-able answer for missing values in a dataset (Osman et al., 2018). Imputationmethods can be classified into two categories: traditional data analysis andmodern data analysis (Osman et al., 2018). When the rate of missing valuesis low, traditional data analysis methods like deletion (listwise and pairwise)and single imputation (mean, hot-deck/cold-deck, and regression) are useful.Deletion technique create a complete dataset by removing records that havemissing value (Schafer and Olsen, 1998). Although the deletion techniqueis simple to use, the sample size reduction it causes can lead to a decreaseof statistical power (Mcdonald et al., 2000). Modern data analysis, on theother hand, is used when the rate of missing values is high and includes mul-tiple imputation (iterative robust model-based imputation (IRMI); multipleimputations of incomplete multivariate data (AMELIA); sequential impu-tation for missing values (IMPSEQ)), model-based procedures (expectation-maximization algorithm), and machine learning techniques (K-nearest neigh-bour (KNN), decision trees and neural networks) (Graham, 2009; Osmanet al., 2018).Acuna and Rodriguez (2004) evaluated the performance of KNN andlinear discriminant analysis classifiers on twelve datasets where the missing102.3. HANDLING CORROSION DATABASEvalues were imputed using the deletion method, mean, median, and KNN.For a small missing percentage, not significant difference is found betweenthe deletion technique and other imputation methods. However, KNN im-putation outperformed the mean and median imputation techniques as themissing rate increased. To deal with incomplete data in the water distribu-tion network, Kabir et al. (2019) used single imputation (mean, median, andlinear regression) and multiple imputation (IRMI, AMELIA, and IMPSEQ)approaches. The study’s findings suggested that multiple imputation tech-niques are superior to single imputation techniques. Betrie et al. (2016) usedthe same techniques to estimate the missing values in a database of waterquality. According to the study, IMPSEQ and IRMI work better in estimat-ing the missing values. Laakso et al. (2018) used random forest to estimatethe missing values in a sewer database in southern Finland.Corrosion prediction models have a key role in the implementation ofpreventive strategies. The prediction accuracy of corrosion models can beenhanced by solving the challenges of missing values. The corrosion databasecollected by National Bureau of Standards (NBS), for example, completesoil data is available for less than half data (Ricker, 2010). As a result,imputation techniques must be used to increase model accuracy in corrosionprediction.2.3.2 Imbalanced datasetA classification algorithm’s performance is negatively impacted by animbalanced dataset, a serious data quality problem. When the percentageof a majority class in a dataset greatly exceeds the percentage of a minorityclass, the dataset is said to be imbalanced (Alhakbani, 2019). The differ-ence becomes problematic when the cost of misclassifying a minority class issignificantly higher than the cost of misclassifying a majority class (Abd El-rahman and Abraham, 2013). For example, the cardiac surgery dataset inYap et al. (2014) has imbalanced samples of died (4.2%) and alive (95.8%).In a typical case, the classifier assumes that the class distribution is equal andthat the cost of misclassification is similar across all classes. When a classi-fication algorithm is applied to an imbalanced dataset, such as the cardiacsurgery dataset, the classifier will, nevertheless, be biased toward the major-ity class (alive) and will not accurately identify the important minority class(died). The problem of class imbalanced datasets, where the minority classis a class of interest and frequently misclassified, is prevalent in a variety offields, including text categorization, fraud detection, oil spill detection, andmedical diagnosis (Babar and Ade, 2016; Rout et al., 2018; Effendy et al.,112.3. HANDLING CORROSION DATABASE2014; Zheng et al., 2004; Kotsiantis et al., 2006; Alhakbani, 2019).Different handling techniques have been suggested to address the classimbalance problem. These include data level approach (such as randomoversampling, random undersampling, and feature selection), algorithm levelapproach (such as adjusting the costs of various classes, adjusting the deci-sion threshold, and ensemble methods), and hybrid methods (Spelmen andPorkodi, 2018; Yap et al., 2014; Cao et al., 2013; Harliman and Uchida, 2018;Rout et al., 2018; Wang et al., 2020). Different sampling methods are ap-plied at the data level approach to balance the skewed class distribution.Contrarily, the algorithm level approach adjust the misclassification costs ofvarious classes rather than using sampling techniques to train the algorithmto overcome bias toward the majority class (Abd Elrahman and Abraham,2013). Additionally, bagging and boosting based ensemble methods are an-other widely used technique to address imbalanced problems (Sun et al.,2015). Hybrid approaches combine both the data level and algorithm levelapproaches (Estabrooks et al., 2004).The data level approach’s resampling techniques balance the samplespace for an imbalanced dataset to reduce the effects of skewed class distri-bution on learning (Haixiang et al., 2017). The two categories of resamplingtechniques are random under-sampling and random over-sampling (Wanget al., 2020). Through the removal of samples from the majority class, theunder-sampling technique balances the skewed class distribution. The ma-jor drawback of this method is that it discards useful information that couldbe important for the induction process. Additionally, this approach limitsclassifiers to estimate the probability distribution of the target populationusing a sample distribution, where many examples of the majority class areremoved from the dataset (Kotsiantis et al., 2006). On the other hand,oversampling techniques, which replicate examples from the minority class,reduce the impact of the skewed class distribution. Since it duplicates theminority class instances exactly, the oversampling technique’s disadvantageis that it may increase the likelihood of overfitting. In addition, if the datasetis already big and imbalanced, oversampling can add additional computa-tional task (Abd Elrahman and Abraham, 2013; Kotsiantis et al., 2006). Tosolve that problem, the effect of skewed distribution is balanced by creatingnew samples that belong to the minority class rather than copying. This is aspecial type of oversampling. Synthetic Minority Over-sampling Technique(SMOTE) is the most frequently used technique for this method (Chawlaet al., 2002). The SMOTE technique generates artificial data by selectingrandom sample from the minority class and introducing synthetic samplesalong the line joining the random sample and all the K nearest neighbors122.3. HANDLING CORROSION DATABASEof the same class. Outside the border, SMOTE doesn’t create instances.This technique focuses on low dimensional data, and has limited applicationto a multi-class high dimensional data (García et al., 2012). When thereare not enough instances of the minority class, this technique also has thedrawback of inaccurately estimating the probability distribution of the ac-tual data (Abd Elrahman and Abraham, 2013). Finally, feature selectiontechnique is also used to choose a small subset of the relevant features ina high dimensional dataset when there is class imbalance. This increasespredictive accuracy while simultaneously reducing over-fitting, data storage,and other problems.In an algorithm-level approach, techniques focus on modifying the classi-fiers’ algorithms to reduce their bias towards the majority class rather thanchanging the training data. This requires a thorough understanding of themodified algorithm and a precise identification of the reasons of its inabilityto mine skewed distributions (Fernández et al., 2018). An algorithm-levelapproach called cost-sensitive learning handles class imbalance by takinginto account the cost of incorrectly classifying minority class samples (Haix-iang et al., 2017; Harvey and McBean, 2014c). Compared to resamplingtechniques, it is computationally efficient, but it has a significant drawbackwhen it comes to assigning misclassification cost (Haixiang et al., 2017).Some machine learning classifiers, such as Naïve Bayes and neural networks,provide a score that indicates how closely an instance belongs to a givenclass. Therefore, by adjusting the threshold of an example relevant to a class,the performance of the classifier on an imbalanced dataset can be improved(Kotsiantis et al., 2006). The ensemble-based method, which combines theoutput or performance of multiple classifiers to enhance the performance ofa single classifier, is also used to manage imbalanced datasets. Bagging andboosting are the most widely used and successful ensemble algorithms (Routet al., 2018). The disadvantage of ensemble methods is that they may over-fit as well as use sampling approaches to get balanced data throughout eachiteration of the process, which may result in the elimination of some usefuldata (Sun et al., 2015). The hybrid method either combines over-samplingand under-sampling approaches, or it combines data-level and algorithm-level approaches to reduce the impact of class imbalance (Galar et al., 2011;Estabrooks et al., 2004). The goal is to improve classification performanceby combining the benefits of both sampling techniques while reducing theinherent drawbacks of each individual technique (Seiffert et al., 2009).Yap et al. (2014) applied oversampling, undersampling, bagging andboosting methods to handle an imbalanced cardiac surgery dataset. Theclassification performance of a decision tree classifier is found to be en-132.3. HANDLING CORROSION DATABASEhanced by oversampling and undersampling. To oversample the minorityexamples near to the borderline, Han et al. (2005) proposed the borderline-SMOTE method. The experiment’s results were superior to those of SMOTEand random oversampling. Vuttipittayamongkol and Elyan (2020) proposedneighbourhood-based undersampling framework to find and remove instancesof the majority class from the overlapped area. Other data characteristics,such as class overlaps and small disjuncts, in addition to class imbalance,appeared to have an impact on the classifiers’ performance, according to anumber of earlier research (Vuttipittayamongkol et al., 2018; García et al.,2008; Stefanowski, 2013; Sun et al., 2015). Fiorentini and Losa (2020) pro-posed random undersampling to handle an imbalanced dataset of road crashseverity. Four distinct models—random tree, k-nearest neighbour, logisticregression, and random forest—are trained using the original and balanceddataset. The results showed that the undersampling method improved theability of the classifiers to identify fatal crashes. Guo et al. (2020a) proposeda meta learning-based approach that reassigns weights to the training datato handle the problem of class imbalance in a façade image showing defects.The proposed approach increased the convolutional neural network’s classifi-cation accuracy from 71.43% to 82.86%, demonstrating a strong capacity tohandle a dataset that is imbalanced. Bagging outperformed other ensembleapproaches (boosting and random forest) and the baseline classifiers (NaïveBayes and log regression) in Sanabila and Jatmiko (2018) evaluation of howwell ensemble learning handled imbalanced financial data. Estabrooks et al.(2004) recommended combining various resampling techniques as a practicalapproach to the issue of class imbalance.In civil infrastructure assets, imbalanced dataset are observed in sewerpipes (Harvey and McBean, 2014c,a), water pipes (Bagriacik et al., 2018),culvert (Gao and Elzarka, 2021), bridges (Okazaki et al., 2020). Okazakiet al. (2020) proposed an undersampling technique for deterioration mod-eling of an imbalanced dataset of inspection concrete bridges. Harvey andMcBean (2014c) solved the negative impact of class imbalance on predictiveperformance by adjusting the predicted probability cutoffs. The sewer pipesare first categorized into those with good structural condition and thosewith poor structural condition, and then the receiver-operating characteris-tic (ROC) curve is used to specify cutoffs for the predicted class probability.Dang et al. (2021) proposed an ensemble-based strategy and a cost-sensitivelearning-based method to deal with the imbalanced data problem in a relatedstudy on the classification of sewer defects. Harvey and McBean (2014b) con-tains similar work on sewer imbalanced data. Harvey and McBean (2014a)proposed undersampling and threshold adjustment techniques to assess the142.3. HANDLING CORROSION DATABASEpotential to improve the predicted accuracy for pipes in bad condition thatrepresent the minority class of interest. Threshold adjustment is found toprovide an optimal level of performance for the classification problem. Gaoand Elzarka (2021) developed a decision tree algorithm with the SMOTE tohandle with the severely skewed data in the culvert inventory database.The oversampling process can also be implemented using a generativemodel that captures the actual data distribution (such as generative ad-versarial networks (GAN)). GANs are framework designed to train implicitgenerative models using neural networks (Goodfellow et al., 2014). GANapproximate the underlying distribution of real data in order to generatesamples that have similar distribution with the real data. Moreover, GANhandles the class imbalance in a dataset by generating new data samplesbased on the joint probability of the input data and labels (Gao et al.,2019). Mirza and Osindero (2014) has proposed a GAN improvement calleda conditional generative adversarial network (cGAN). Douzas and Bacao(2018) proposed cGAN model to approximate the original data distributionand generate synthetic data to augment the instances pertaining to minorclass in various imbalanced datasets. The performance of cGAN is comparedwith other oversampling algorithms, and the result showed that cGAN canimprove the quality of the generated data significantly. Wang et al. (2020)proposed a novel traffic data augmenting method called PacketcGAN us-ing cGAN. The model compared with other data augmentation techniques(such as random over-sampling, SMOTE and GAN) and the experimentalresults showed that the encrypted traffic dataset augmented by Packetc-GAN achieved a higher classification accuracy than the remaining in termsof encrypted traffic classification. Engelmann and Lessmann (2021) pro-posed a method for modelling tabular credit score datasets with numericaland categorical variables using an oversampling approach that is based on aconditional Wasserstein GAN. The study found that on most datasets, GAN-based oversampling outperformed random oversampling and SMOTE-styleapproaches. Son et al. (2020) proposed a Borderline-Conditional GenerativeAdversarial Networks technique to generate minority class data, particularlyaround the dividing line between majority and minority classes. Their eval-uation of a number of imbalanced datasets indicates encouraging findings.In both classification and regression tasks, an imbalanced dataset is asignificant problem (Yang et al., 2021). By addressing the problems withimbalanced data, corrosion models’ prediction accuracy can be improved.The corrosion database collected by Velázquez et al. (2009), for example,have imbalanced samples of observation for the different soil types. As aresult, adopting data augmentation techniques is necessary to increase the152.4. CORROSION PREDICTION MODELSmodel’s predictive capability.2.4 Corrosion prediction modelsResearchers have developed a number of models for estimating corrosiondefect size, and as a result, the defect size is used as an input parameter forpipeline reliability assessments. However, because of the intrinsic complexityof the corrosion process, estimating corrosion rate is subject to uncertainty.Using a model that is appropriate for the pipeline conditions can reducethe uncertainty in estimating corrosion rate. A pipeline leak or rupture canoccur when a very low corrosion rate is predicted, but a high corrosion ratemay necessitate unnecessary pipe assessment (Vanaei et al., 2017).Vanaei et al. (2017) has thoroughly examined the deterministic and prob-abilistic models developed to estimate corrosion growth rate. Deterministicmodels include single-value corrosion growth rate, linear corrosion growthrate, and non-linear corrosion growth rate (Vanaei et al., 2017). The Na-tional Association of Corrosion Engineers (NACE) advises using empiricalvalues depending on the corrosiveness of the media when estimating the av-erage corrosion rate using the single-value corrosion growth rate. NACE rec-ommends using a pitting rate of 0.4 mm/y to calculate re-inspection intervalsin the absence of data (Vanaei et al., 2017; Valor et al., 2013b). Accordingto the linear growth model, the depth of a corrosion defect acts as a linearfunction of time; as a result, the corrosion rate must be determined usingtwo sets of inspection data collected at different times. Stochastic modelsinclude poisson square wave process (Bazán and Beck, 2013), Markov chainmodelling (Provan and Rodriguez III, 1989; Caleyo et al., 2009a; Valor et al.,2013a), Weibul distribution (Sheikh et al., 1990), Time-dependent General-ized Extreme Value Distribution (Caleyo et al., 2009b), and Gamma process(Velázquez et al., 2014). The transitional probabilities between corrosion pitstates are represented in the Markov modelling approach as functions of en-vironmental modelling (Seghier et al., 2020). The probabilistic methods arefound to be more robust than the deterministic methods because of inherentuncertainties in the corrosion process and in operational conditions (Bazánand Beck, 2013).The majority of corrosion pit depth models developed after 1969 arebased on the equation dmax = k(t)v proposed by Romanoff (1957), wherethe author suggested a power law function to model maximum pit depth(dmax) as function of exposure time (t) and constant parameters (k and v).The parameters change depending on the type of soil environment. Numer-162.4. CORROSION PREDICTION MODELSous subsequent studies have examined how the regression parameters dependon various soil characteristics (e.g., pH, soil resistivity) (Mughabghab andSullivan, 1989; Velázquez et al., 2009). The power law approach is conservedin these studies, except soil factors are added to account for the corrosionenvironment (Balekelayi and Tesfamariam, 2020). Mughabghab and Sulli-van (1989) indicated that the pitting parameter k is mainly influenced bypH, and soil resistivity plays a minor role in its prediction. The exponent vis found to be influenced by moisture and clay content of the soil. Katanoet al. (2003) proposed utilizing multivariate regression analysis to predict themaximum pitting depth of a cast iron pipe using a number of environmentalparameters, such as soil type, history of land preparation, soil resistivity, pH,redox potential, sulfides, and exposure period. Despite the authors’ signifi-cant contributions, important factors that affect the corrosion process—suchas pipe coating and pipe to soil potential—are not taken into considera-tion. Velázquez et al. (2009) developed a model for prediction of maximumpitting depth as a function of exposure time, soil and pipe characteristics.The parameters k and v are estimated as a linear combination of soil andpipe characteristics, under a constraint that each predictor variable couldbe associated with only one parameter. Norhazilan et al. (2012) developeda regression model to predict corrosion of carbon steel using soil properties(moisture content, clay content and plasticity index). The outcome showedthat moisture content alone has an impact on corrosion and that the combi-nation of the three soil variables was unable to accurately capture the cor-rosion rate. Balekelayi and Tesfamariam (2020) proposed Bayesian spectralanalysis regression model to predict corrosion pitting depth on bare pipelinesusing several soil characteristics. The proposed approach demonstrated thenonlinear effect of covartiates on the pitting depth. Biezma et al. (2018) pre-dicted external corrosion rate based on combination of six soil parametersmeasurement and corrosion rate data. The proposed method can be helpfulto industry because it lowers the cost of inspections. Rather than using alinear or power law function to explain the data trending in corrosion, Melch-ers and Petersen (2018) reinterpreted Romanoff (1957) data from 1957 andproposed a bi-modal approach. A nonexhaustive summary of soil and pipefactors used in the corrosion study of pipelines is provided in Table 2.2.Machine learning-based models are effective for capturing the nonlinearbehavior of complex problems like corrosion (Ossai, 2020; Jančíková et al.,2013; Abbas et al., 2018; Cai et al., 1999). Rachman et al. (2021) has con-ducted an exhaustive review on machine learning application for pipelineintegrity management. For predicting the maximum corrosion pit depth inoil and gas pipelines, Seghier et al. (2020) developed a hybrid artificial intel-172.4. CORROSION PREDICTION MODELSTable 2.2: Summary of soil and pipe factors affecting the corrosion of oil andgas pipelines.Reference ExposuretimeInternalDrainageBurialDepthSandSiltClayColloidSuspensionResistivityMeanTemperaturePrecipitationMoisturePorositySpecificGravityVolumeShrinkagepH RedoxpotentialPipe-Soil-PotentialSoilbulkdensityChloridecontentBicarbonatecontentSulfatecontentSulfidesCoatingtypeRomanoff (1957) XMughabghab and Sullivan (1989) X X X X XVelázquez et al. (2009) X X X X X X X X X X XBiezma et al. (2018) X X X X X XKatano et al. (2003) X X X X X X X XBalekelayi and Tesfamariam (2020) X X X X X X X X X X X X X X X XNorhazilan et al. (2012) X Xligence model based on support vector regression (SVR). Optimization algo-rithms, such as practical swarm optimization, genetic algorithms, and fireflyalgorithms, are used to choose the SVR’s optimal parameters. Seghier et al.(2021) provided a novel framework based on data-driven techniques, such asartificial neural network, M5 Tree, multivariate adaptive regression splines,locally weighted polynomials, kriging, and extreme learning machines, fordetermining the maximum corrosion pit depth in the Velázquez et al. (2009)database. To determine the variables contributing most to corrosion pre-diction, various combinations of input variables were explored. Accordingto the outcome, adding more input variables generally increases a model’saccuracy. Data scarcity, however, is one of the key obstacles to applyingmachine learning algorithms.The Bayesian network is a popular tool for calculating the probabilityof pipeline failure because of hazards. When the given data is ambiguous,incomplete, or imprecise, a BBN model can be effectively used to make de-cisions (Kabir et al., 2015). Jain et al. (2015) developed a BBN and useddata from in-line inspections (ILI) to validate it in order to quantitativelyestimate corrosion defects in buried crude oil pipeline. Caleyo et al. (2015)proposed a Bayesian analysis technique to estimate the statistical distribu-tions of the density and size of external corrosion defects for a non-piggablepipeline. Wang et al. (2015) developed a Bayesian approach for calibrat-ing and estimating corrosion defect depth based on an ultrasonic ILI andthe clustering technique. Singh and Markeset (2009) proposed a fuzzy logicframework to estimate a rate of corrosion, which can then be applied to risk-based inspection of oil and gas pipes. A Bayesian network is employed inthis dissertation to estimate the depth of corrosion defects in buried oil andgas transmission pipes.182.5. RISK ASSESSMENT2.5 Risk assessmentPipeline integrity programs include pipeline risk management programs,which help pipeline operators in managing risk and optimising safety, re-liability, and cost by taking uncertainties into account rather than strictlyadhering to guidelines. The components of the risk management programinclude risk analysis and risk control. While the former is used to rankpipeline, based on their probability and consequence of failure, the latterused this information to decide the next inspection interval. Prioritizingpipeline segments based on their risk information helps pipeline operatorsallocate resources for mitigation and remediation of the pipes in high risk inan effective manner (Khan et al., 2021).Risk analysis is a key component of the pipeline integrity management.The steps in the risk analysis process are shown in Figure 2.1. Risk is definedas the product of probability of failure due to hazard and it’s associated con-sequences. It is carried out to highlight potential accidents and assess theirimpact to identify and rank prevention and intervention strategies (Shahriaret al., 2012). The first step in the process of risk analysis is hazard identifi-cation. It indicates to an identifying mechanism that can lead to a pipelinefailure with accompanying consequences (Muhlbauer, 2004). Hazards can bebroadly classified into two categories: a) Time-dependent (such as internaland exterior corrosion) and b) Time-independent (such as natural disasters,equipment failure, and third-party damage). In the sections that follow, abrief discussion of the failure analysis and consequence analysis is provided.There is a number of published studies on risk estimation that addresspipeline risk by taking causes, probabilities, and failure consequences intoaccount. The proposed models focuses on either hazardous liquid pipelines(Henselwood and Phillips, 2006; Dziubiński et al., 2006) or natural gaspipelines (Brito and de Almeida, 2009; Jo and Ahn, 2005; Cagno et al.,2000; Brito et al., 2010; Wu et al., 2017; Lu et al., 2015; Shan et al., 2017;Lu et al., 2015; Fang et al., 2019; Han and Weng, 2010), or combination ofboth (Yuhua and Datao, 2005; Pandey, 1998; Li et al., 2020). Models for riskanalysis can also be broadly divided into qualitative, quantitative, and semi-quantitative approaches (Dziubiński et al., 2006). In order to evaluate theprobability and consequences of failure, qualitative risk assessment methodsuse a relative scale (e.g., low, medium, high, and very high) or index systemthat is based on the essential information of the pipeline network. Statisticalanalysis or engineering judgement, depending on the availability of data, isused to determine the scale or relative weight of the risk variables. A quali-192.5. RISK ASSESSMENTHazard IdentificationConsequence AnalysisRisk EstimationFailure AnalysisPipeline Risk AnalysisFigure 2.1: Schematic view of risk analysis components.tative risk value is the outcome of this method (Han and Weng, 2011). Thisapproach has a number of benefits, including the ability to deliver immediateresults, conduct affordable analyses, be comprehensive, and be utilized as adecision-support tool for resource allocation modelling (Muhlbauer, 2004).The most rigorous and complex risk assessment techniques are quanti-tative risk assessment methods, also known as probabilistic risk assessmentor numerical risk assessment methods. They estimate risk through numeri-cal simulation, including a quantitative calculation of failure probability andits corresponding consequence of various accidents. For instance, modelsor functions that describe the dose-effect relationship caused by pressure,heat, or toxicity, along with recipient categories (e.g., death or injuries), areused to evaluate the quantitative risk to individuals and society due to gaspipeline failure (Jo and Crowl, 2008; Muhlbauer, 2004). Due to the highdata requirements of this technique, comprehensive information is requiredto conduct a quantitative risk assessment. Insufficient information makesrisk management using this technique challenging (Gharabagh et al., 2009).It relies heavily on historical failure data (Muhlbauer, 2004). When data isonly partially available, risk can be estimated using semi-quantitative meth-ods that combine the data that is readily available with expert-providedsubjective qualitative information (Zakikhani et al., 2020). The choice ofthe risk assessment method is influenced by a number of factors, includingthe study’s objective, availability of resources, quality of the informationavailable, and complexity of the environment. However, some research stud-202.5. RISK ASSESSMENTies recommend using combination of qualitative and quantitative methodsbecause of it’s efficiency and accuracy (Urbina and Aoyama, 2017). Catego-rizing the literature based on the techniques adopted helps to identify thedata requirements and sources of uncertainty in risk analysis.Khan et al. (2021) has provided an overview of the development of risk-based pipeline integrity management techniques during the last two decades.The advantage of review papers is that it provide insights on a topic basednot only on published papers, but also from standards and recommendedpractices. Cunha (2016) studied the quantitative risk analysis of onshorepipelines that move natural gas, refined products, and oil. The failure rateis estimated using statistics on pipeline failure from the US, Canada, Europe,and Brazil. Based on historical data on the consequences of an accidentalloss of containment for an onshore pipeline, the consequence of failure is es-timated. Statistical analysis of historical data is long being used for pipelinerisk assessment by assessing the causes, consequences, and distribution offailures in a pipeline network (Papadakis, 1999; Restrepo et al., 2009; Lamand Zhou, 2016; Ramírez-Camacho et al., 2017; Bubbico, 2018). Jo andAhn (2005) proposed a quantitative risk assessment method for natural gaspipelines. The authors introduce the fatal length and cumulative fatal lengthparameters to indicate the integrated fatality along the pipeline associatedwith an accident and the section of a pipeline in which an accident resultsin N or more fatalities, respectively. These parameters are obtained frompipeline geometry information and population density of a GIS. The fail-ure rate of a pipeline is estimated based on failure causes (such as externalinterference, construction defects, corrosion, ground movement, and others)and hole size. In the absence of failure data, Zhang et al. (2020) developed amethod for assessing the risk of leakage in heavy oil gathering pipelines. Hanand Weng (2011) proposed both qualitative and quantitative risk assessmentmethods for the urban natural gas pipelines. In the qualitative approach,indices (such as a causation index, an inherent risk index, and a consequenceindex) are selected based on statistical analysis of accident databases, andtheir corresponding weights are calculated in accordance with the reliabilityengineering theory and the grey correlation theory. Similar results are ob-tained by both assessment techniques. Dziubiński et al. (2006) proposed amethod for assessing the risks associated with long pipelines that transporthazardous substances that incorporated qualitative and quantitative tech-niques of pipeline safety assessment. To handle the complexity of a givensystem and changes within over time, Liu et al. (2021) developed a dynamicrisk assessment model based on system dynamics for gas pipelines. Thefailure probability and accident consequences are taken into account when212.5. RISK ASSESSMENTconstructing the system dynamics for the risk assessment. The proposedmethodology effectively characterizes the dynamic characteristics of systembehavior.In order to prioritize pipeline segments and help decision makers in de-veloping the optimal action plan and strategy to reduce the consequencesof pipelines that are at high risk, decision models like fuzzy logic (Jamshidiet al., 2013), Bayesian network (Wu et al., 2017), multi-attribute utility the-ory (Brito and de Almeida, 2009) have been used. In order to rank andassess the risks associated with sections of natural gas pipelines, Brito andde Almeida (2009) proposed a multi-attribute utility theory. Three dimen-sions of consequences namely human, environmental, and financial are con-sidered to represent the outcomes of pipeline failure. This and the study’sapproach, which takes decision makers’ behaviour into account when mak-ing risk management decisions, are helpful for ranking the critical sections.Other studies that suggested a multicriteria decision model for evaluatingpipeline risk include Alencar and De Almeida (2010). Gharabagh et al.(2009) suggests enhancing the quantitative risk assessment with risk rankingmethods in the absence of enough data. The analytical hierarchy process(AHP), a multiple criterion evaluation methodology that enables the in-corporation of both subjective and objective factors in the decision-makingprocess, offers a flexible and simple means of assessing complicated problems(Dey, 2004). Dey (2004) proposed using AHP to predict pipeline failure riskfactors, analyze their impact, and develop solutions through efficient inspec-tion and maintenance strategies. AHP has the drawback of not completelyeliminating subjectivity (e.g., weightage against the failure factors are basedupon experience and perception of the decision makers).A bow-tie model is a graphic approach that has been widely applied insafety management areas. The fault tree (FT) and event tree (ET) are itstwo components, which are located on the left and right part of the bow-tie, respectively. The model displays an entire accident scenario, beginningwith the causes and ending with the consequences. The primary elementsof the bow-tie model are the accident hazard source, critical event, safetybarriers, and failure consequences. Figure 2.2 illustrates the bow-tie model’sstructural layout. The top event on the fault tree is the critical event thatis most likely to occur after identifying the risk variables for the systemto be evaluated. The critical event serves as the root of the event tree,which thereafter follows any possible consequences (Li et al., 2016; Shanet al., 2017). For the risk evaluation of oil and natural gas pipelines, thebow-tie method has been used in a number of studies (Li et al., 2016; Shanet al., 2017; Taleb-Berrouane et al., 2021; Shahriar et al., 2012; Fang et al.,222.5. RISK ASSESSMENT2019). Taleb-Berrouane et al. (2021) proposed an adaptive bow-Tie modelfor quantitative microbiologically influenced corrosion risk assessment fromsafety and economic perspectives. In order to assess the risk of a natural gaspipeline failure, Shahriar et al. (2012) proposed a general framework thatcan accommodate both model and data uncertainty. The causes and con-sequences of an event are represented in a single platform using a bow-tieapproach that combines a fault tree and event tree. The study looked attwo types of uncertainties: uncertainty related to inter-dependencies amongvarious risk events and uncertainty related to the basic risk events at FT andoutput event at ET. Fuzzy logic is used in the study to estimate fuzzy prob-abilities of output event consequences and to derive fuzzy probabilities ofbasic events in the fault tree in order to deal with the vagueness of the data.The study also looked at how inter-dependencies between different factorsmight affect analysis findings and introduced fuzzy utility value to performrisk assessment for natural gas pipelines using triple bottom line sustain-ability criteria, namely, social, environmental, and economic consequences.Lu et al. (2015) suggested combining a risk matrix with a bow-tie modelto develop a comprehensive method of risk evaluation. The study addressesthe bow-tie model’s shortcoming, the absence of quantitative findings, byincluding a quantitative risk matrix that ranks the probability criteria andconsequence severity criteria. The proposed study has been applied to a casestudy involving an underwater natural gas pipe. Shan et al. (2017) proposeda quantitative risk analysis approach for natural gas pipeline by coupling abow-tie and Bayesian network models. The fuzzy causes and consequences ofa gas pipeline leak are mapped from a bow-tie model to a Bayesian networkusing fuzzy logic. For a quantitative risk analysis method of a natural gaspipes in a utility tunnel, Fang et al. (2019) used a similar approach, cou-pling a bow-tie model with a Bayesian network. Bayesian network has beenpreviously used for risk analysis of oil and gas pipelines (Li et al., 2016; Wuet al., 2017; Shan et al., 2017; Fang et al., 2019). Chalgham et al. (2020)proposed a prognosis and health monitoring (PHM) approach for managingthe integrity of the gas pipeline system. The framework is embedded in asoftware platform. GIS software has also been used in the risk assessmentof oil and gas pipelines (Ma et al., 2013; Jo and Ahn, 2005; Cheng et al.,2015). The risk analysis framework in this dissertation is based on the bow-tie approach. The components of risk are further discussed in subsequentsub-sections.232.5. RISK ASSESSMENTBow-tieFault tree Event treeConsequence assessmentFailure assessmentFigure 2.2: Structure of a bow-tie model.2.5.1 Probability of failureUnintentional pipeline contents leakage or integrity loss are both consid-ered pipeline failures. In this dissertation, a pipeline failure mostly refers toa leak or rupture. This can occur when the pipe is subjected to stresses (suchas internal pressure, soil overburden, extreme temperatures, external forces,and fatigue) beyond its capabilities, or if the pipe loses material due to cor-rosion or mechanical damage (Muhlbauer, 2004). One of the key steps in therisk assessment process is to estimate the failure probability using statisticaltechniques or professional judgement. Zakikhani et al. (2020) provided acomprehensive review of failure prediction models for oil and gas pipelines.Fault tree analysis is a logical and diagrammatic method for evaluatingfailure probability of an accident. It is frequently used to assess the reliabilityof complex systems. To predict the precise failure probability of components,however, is often challenging due to incomplete data and ambiguous eventcharacteristics (Shahriar et al., 2012). To predict the failure probabilityof oil and gas transmission pipelines, Yuhua and Datao (2005) presenteda fuzzy fault tree analysis. The authors combined expert elicitation withfuzzy set theories to evaluate probability of events. This method is usefulto treat fuzzy events of a fault tree analysis. For the purpose of evaluatingthe safety of oil and gas pipelines, Kabir et al. (2016) presented a fuzzyBayesian belief network. The developed model aids in evaluating oil and242.5. RISK ASSESSMENTgas pipeline failure in order to deal with uncertainties such as randomness,vagueness, and ignorance. Ahmed and Kabir (2021) has developed failureanalysis frameworks using a variety of hierarchical structures in an effort tomanage the natural gas pipeline efficiently.Corrosion is one of the major causes of failure, especially buried pipelinesare prone to external corrosion rather than internal (Zardasti et al., 2015).Serious consequences, such as fatalities, monetary loss, and environmentalharm, result from pipeline failure brought on by corrosion attack. Severalmodels are proposed to predict the failure probability of pipelines due tocorrosion. Ahammed (1997) proposed a deterministic model to evaluate theremaining strength of corroded pressurised pipelines as well as to predictthe maximum allowable operating pressure for the pipelines at any point inthe future. The methodology is based on corrosion spreading to the thick-ness of the pipe wall that has not been damaged. Prediction of a pipeline’sremaining strength is helpful because it may reduce the need for regularevaluations of the pipeline’s remaining strength, continual monitoring, andunnecessary early repairs. The study used a deterministic methodology toestimate a pipeline’s remaining strength after corrosion. Deterministic ap-proaches, however, do not take into account the uncertainty regarding theinput parameters and are therefore not completely accurate in reality. Theabsence of data, simplification of the model, or the variables’ inherent ran-domness are all factors that contribute to parameter uncertainty (Ahammedand Melchers, 1996). Ahammed (1998) developed a probabilistic methodfor estimating a pipeline’s remaining life when there are active corrosiondefects. A normal and non-normal probabilistic distributions were used torepresent the variability of the load and resistance parameters. The overallresult indicated that pipeline reliability decreases as exposure period getslonger. The contribution of various random variables to the variance ofthe failure probability was also evaluated, and it was found that some vari-ables (such as pipe diameter, defect length, and longitudinal corrosion rate)had low contributions for various exposure duration. Variables having lowvariance contribution might be regarded as deterministic variables. Similaranalyses of the reliability of corroded pipelines were done by Ahammed andMelchers (1996). For estimating the remaining life of pressurized pipelineswith active corrosion, Caleyo et al. (2002) proposed a probabilistic model.The first-order second-moment iterative reliability method, Monte Carlo in-tegration, and first-order Taylor series expansion of the limit state function(LSF) were used to predict the failure probability associated with the cor-rosion defect over time. A dynamic Bayesian network model was developedby Xiang and Zhou (2020) to quantify the progression of corrosion defect252.5. RISK ASSESSMENTdepths on pipelines and predict the time-dependent failure probability ofthe pipeline. The study combined the corrosion growth model, reliabilityanalysis and measurement error quantification of ILI tools in the single dy-namic Bayesian network model. To calculate corrosion defect depth, theauthors used a simple linear corrosion growth model; however, this can beextended to include other corrosion models, such as power-law (Xiang andZhou, 2020). In their innovative work, Chakraborty and Tesfamariam (2021)used the subset simulation (SS) method to analyze the reliability of corrodingpipelines in terms of both space and time. Mahmoodian and Li (2017) pro-posed an analytical reliability-based methodology using first passage proba-bility theory for failure assessment of corrosion affected oil and gas pipelines.Pandey (1998) suggested a probabilistic approach to estimate the reliabil-ity and expected service life of existing pipelines exposed to corrosion. Thestudy used reliability-based criteria to determine the optimal inspection in-terval and repair strategy that would maintain sufficient reliability over thepipeline’s remaining service life. Valor et al. (2013b) used different corrosionrate distributions obtained from various models to analyze corrosion defectdepth and reliability of buried pipelines. The Markov model was found tobe the most accurate at predicting corrosion defect depth evolution and reli-ability. The integrity parameters of corroded oil and gas pipelines have alsobeen widely predicted using machine learning-based methods (Soomro et al.,2022; Sinha and Pandey, 2002).Due to the difficulties of developing such models, the majority of the pro-posed models are built and computed at the component level (pipe segment),and there are few research that focus on system-level (Chalgham et al., 2020).Pipeline operators need to make cost effective and timely decisions to preventaccidents in high consequence areas and, as a result, they need a tool that canintegrate data from a vast network of pipes that extends hundreds of miles inlength and their environment to indicate the failure probability of the pipeassets. Integrity management frameworks must include data integration asa major component. To assist pipeline operators in assessing whether thedanger of an incident is significant or not, all pertinent information that af-fects the failure and consequence assessment process must be gathered fromvarious sources (Chalgham et al., 2020). By combining Bayesian networkswith GIS to develop a failure assessment model, the current dissertation at-tempted to overcome this limitation. The proposed methodology uses spatialsoil and pipeline network data as an input to a corrosion model created ina Bayesian network to handle the aforementioned difficulty. The Bayesiannetwork’s ability to reason and the GIS’s spatial capabilities work togetherto create a strong tool for calculating the probability that pipes in the study262.5. RISK ASSESSMENTarea will fail.2.5.2 Consequence of failureThe consequences of pipeline failure are evaluated on the basis of humanloss, environmental damage, asset loss and financial loss brought on by sup-ply interruption (Shahriar et al., 2012). Some studies also consider indirectlosses like reputational damage because they can cost the pipeline operatormoney (Zardasti et al., 2015). The severity of the consequence varies de-pending on the type of failure (rupture, minor or major leak), mechanicaland operational characteristics of the pipeline, the location of the accident,and other factors. Based on historical records of accidents found in the US-DOT database, Bubbico (2018) presented a statistical report on the causesand consequences of the release of hazardous materials (such as crude oil,natural gas, and oil refined products) from pipelines.Consequence assessment can be done using a qualitative and quantitativemethods depending on the intended goal of the analysis. In considerationof human safety, environmental damage, and monetary loss, DNV (2010)provided a qualitative guidelines for assessing the potential consequences ofaccidental events to pipelines. The human safety consequence is establishedbased on the safety of personnel. The environmental consequences is estab-lished for both minor and major release scenarios and concentrates on thepolluting effects brought on by pipeline leaks. This is determined by thequantity and type of the spill, the weather, and the travel time to reachvulnerable areas. Any damage’s financial impact is categorised based on thepotential delay in pipeline production and the cost of repair. The cost ofrepairing pipeline damage, however, was not included in the evaluation be-cause it was so small. All of the consequences listed in the guidelines aresubjective and require expert judgement.Restrepo et al. (2009) studied how the various accident causes and char-acteristics affect the cost-related consequence measures in order to assessthe risk implications of hazardous liquid pipeline incidents. The followingeconomic consequence measures are examined: value of the product lost;public, private, and operator property damage; and clean up, recovery, andother costs. The suggested methodology consists of two steps. The proba-bility of a nonzero consequence is calculated in the first step as a functionof the accident’s characteristics, such as explosion, product ignition, amountof liquid lost, the area of occurrence, the system component involved andcause of accident. The factors associated to nonzero costs of product loss,property damage, cleanup, and recovery are determined using a logistic re-272.5. RISK ASSESSMENTgression model. The characteristics of the consequence measures are assessedin the second step in relation to the accident’s characteristics. The study’sfindings showed that the volume of liquid spilled is a good predictor of alloutcomes, including the cost of product loss, property damage, cleanup andrecovery efforts, and whether an evacuation is required. Parvizsedghy andZayed (2016) suggested using a bowtie-based neurofuzzy model to calculatethe financial impact of gas pipeline failures. This model was developed us-ing historical failure data for gas pipelines. The model’s input parametersare pipes’ diameter, wall thickness, specified minimum yield strength, andmaximum operating pressure. Additionally, two variables are used as inputparameters: the failure scenario and the location category. Since it is difficultto estimate the actual financial consequence value, a scale of 1 to 11 is used toestimate it’s level between minimum and maximum cost. The end outcomehad a 78% accuracy between the testing dataset’s predicted and actual lev-els of financial consequences. The ET in the study of Shahriar et al. (2012)considered various output events of the critical event (e.g., gas release). Thetime interval between the gas leak and possible ignition as well as the degreeof space confinement caused by the environment were identified as the ma-jor considerations that went into constructing the ET. Detonation, fireball,confined vapor cloud explosion, flash fire, and material loss were the outputevents of the gas release. The triple bottom line sustainability criteria wereapplied to evaluate the social, environmental, and economic consequencesof a gas release accident after the likelihood of each of these events occur-ring was determined. The social criteria for consequence includes humancasualties, evacuation, and the response of society to accidents. The envi-ronmental consequence includes air pollution, the destruction of vast areasof vegetation, loss of habitat for endangered species, and water pollution.The economic consequence includes supply interruption, property damage,loss in revenue due to loss of gas, and expenses required for labor, equip-ment and raw material to replace or repair affected pipes. Fuzzy syntheticevaluation was used to estimate the severity of the consequences for each ofthe expert-provided criteria.Chen et al. (2019) proposed a method for assessing the overall conse-quence of a pipeline on the oil and gas industry while taking into account allkinds of losses. The assessment includes the following steps: identification ofan accident scenario (such as a fire, explosion or toxic discharge), determiningthe influence area (including calculation of oil and gas release rate, physicaleffect analysis and influence area estimation) and calculating possible dam-ages. In a quantitative risk assessment, calculating possible losses is helpfulfor determining risk. The losses taken into account in the study are produc-282.5. RISK ASSESSMENTtion loss, asset loss, human health and safety loss and environmental cleanupcost. By integrating the four aspects of release accident loss into a quantita-tive model, the study was able to address the drawback of earlier quantitativerisk assessment methodologies. Bonvicini et al. (2015) developed a detailedprocedure for assessing the environmental risks posed by significant accidentscaused by oil and liquid hydrocarbon spills from on-shore pipelines. To ex-press the risk of soil and groundwater pollution, the study defined specificenvironmental risk indices in physical and economic terms. The proposedmethodology was viewed as an important first step in developing a compre-hensive approach for managing risk associated with on-shore pipelines. Boot(2013) gave a comparison of the transportation of propane (100,000 t/year)using various means of transportation, including rail, road, ship and pipelinewith diameter 65mm. The study’s result indicated that, when incidents withless severe consequences were taken into account, pipeline mode was morehazardous than other forms of transportation. For more severe accidents, itwas, however, much safer. Ramírez-Camacho et al. (2017) studied the con-sequences of pipeline accidents and it’s influence on land-use planning. Apipeline failure caused by human activity can have serious consequences inareas where people live, and it can also have serious environmental impactsby polluting the soil or water in areas where people don’t live. The quantityof product released from the pipeline has a direct relation with the severityof the pollution.Wu et al. (2017) developed a framework to probabilistically analyse a nat-ural gas pipeline network based on the investigation of gas pipeline networkaccidents and expert opinions with weighted treatments by the Dempster-Shafer evidence theory. The framework was able to capture the evolutionprocess of the network from failure causes to consequences. To evaluate theimpact of buried municipal infrastructure and assist the owners of these as-sets in prioritising these infrastructures for maintenance, rehabilitation, andreplacement, Kabir et al. (2018) proposed a BBN-based consequence model.This is particularly useful for small and medium sized utilities that oftendoesn’t have sufficient data and lack the needed technical, financial and ex-pert resources to assess the likelihood of failure assessment (Kabir et al.,2018). In the absence of a deterioration model to predict the conditionof buried infrastructure, these underground assets are identified, evaluated,and prioritised based on the consequences of failure, which can assist in esti-mating and reducing the risk of buried infrastructure systems (Kabir et al.,2018). Belvederesi et al. (2018) examined data on pipeline accidents pro-vided by PHMSA between 2010 and 2017, with an emphasis on the effects ofhazardous liquid pipeline accidents on the environment. The frequency and292.5. RISK ASSESSMENTseverity of the impact of discharged product on soil, water, and wildlife areexamined. According to the study’s findings, 85% of the product releasedafter an accident was not recovered, 53% of accidents contaminated soil,41% of accidents had an impact on environmentally sensitive areas, and 92%of water crossing pipelines involved in accidents were uncased. Yang et al.(2022) proposed a data-driven model based on graph embedding and deepclustering for post-event risk assessment of an accident consequence of urbangas pipeline network. Models that describe the time-varying consequence ofan accident are preferable than the traditional static risk analysis methods.Qiu et al. (2018) developed a model based on catastrophe theory and dy-namic Bayesian network model for tracing and prediction study of an urbanpipeline leaking accident. The proposed method is applied to the Kaohsiungexplosion accident, and the model’s output produced results consistent withthe real scenario on identifying the direct cause of the accident. Additionally,this method predicts the accident’s evolution, which is helpful in providingscientific guidance for preventing accidents and emergency rescue.In the oil and gas industry, GIS has been used to assess the consequenceof oil spill and gas leaks (Farrar et al., 2005; Nwankwoala and Nwaogu,2009; Das and Harinarayana, 2014). Das and Harinarayana (2014) exam-ined aspects related to possible gas leaks and oil spills using a GIS applica-tion. To find possible hazardous locations and vulnerable zones, a numberof databases were used, including those for roads, rails, rivers, elevation,pipeline networks, fault zones, earthquake zones, meteorological, and others.Cheng et al. (2015) simulated the spread of a single point fire on an urbannatural gas pipeline using GIS spatial analysis. An integrated BBN-GISframework is developed in this dissertation to model and estimate the effectsof pipe failure caused by corrosion threats.30Chapter 3Bibliometric analysis for riskassessment of onshore oil andgas pipelines3.1 BackgroundNumerous research papers on risk analysis of oil and gas pipelines havebeen published. The current study includes a bibliometric analysis for riskassessment of oil and gas pipelines by taking into account publications pub-lished between 1982 and 2022 in order to give a broad picture of the researchcontribution in this area. The published articles in this field are studied us-ing a variety of methods, including as trend analysis, bibliographic coupling,co-occurrence analysis, network analysis, and citation analysis. Based on theresearch’s keywords, the co-occurrence analysis reveals the strong and weakconnections between various topics in this domain, and as a result, futureresearch areas can be identified.3.2 Biblometric analysisBibliometric analysis aids in the identification of significant authors, insti-tutions, and nations for their contributions to the body of existent literatureon a certain subject (Xue et al., 2018; Khanra et al., 2020). The method wasintroduced by Pritchard (1969), and it has since become a popular scien-tific approach for analyzing the temporal evolution of a research field from amultidisciplinary perspective (Tandon et al., 2021). In contrast to the tradi-tional way of research, which involves analyzing the literature in a narrativemanner, bibliometric analysis offers a comprehensive summary of a subjectof intellectual interest, defines its boundaries, and suggests research agen-das by revealing statistical trends with less subjectivity. It has been usedto analyze the intellectual structures of research topics on sewer pipelines(Salihu et al., 2022), asset management (da Silva and de Souza, 2021), oil313.3. RESEARCH METHODSStep 1. Defining research questionsStep 2. Defining the list of search sourcesStep 3. Defining search query based on keywordsStep 4. SearchingStep 5. Assessing quality of resultsStep 6. Bibliometric analysis of search resultsStep 7. Results and discussionTo understand existing literature and future trends onthe topic of risk related to oil and gas pipelinesWeb of Science (https://www.webofscience.com/wos/woscc/basic-search)Search strings is given in Table 3.1Documents identified are 2466, including articles, proceeding paper, review article, early access and editorial material.Filtering of search resultsi. Publication year between 1982 and 2022ii. Journal articles and review article onlyiii. Excluding articles published in certain categories, such as oceanography,engineering marine, nuclear science technology, telecommunications, etc.iv. Language (English)A bibliometric review byi. Year of publicationii. Countriesiii. Authorsiv. Institutionsv. Author’s keywordsFigure 3.1: Research design for bibliometric analysis of retrieved papers.and gas pipeline failure consequence analysis (Aalirezaei and Kabir, 2021),building information modelling (BIM) for structural engineering (Vilutieneet al., 2019), and BIM and GIS integration for infrastructure asset manage-ment (Garramone et al., 2020). A bibliometric analysis is conducted in thisdissertation to investigate risk assessment of onshore oil and gas pipelines.3.3 Research methodsFigure 3.1 shows the research approach used to review articles on therisk assessment of oil and gas pipelines. The current bibliometric studyadopts a similar methodology to Vilutiene et al. (2019), in which bibliometricanalysis is carried out to outline the intellectual structure, gaps within, andfuture prospects on a research topic. The web of science (WoS) database ismined for published papers relevant to the research topic based on searchstrings. The most relevant papers are then found by filtering the search andperforming a criteria-based analysis on the dataset. Figure 3.1 illustratesthe research process as follows:1. Defining Research Questions. The scope of the research can be spec-ified based on the type of study. The objective of this research is toidentify and review the literature in the field of risk assessment of oil323.3. RESEARCH METHODSand gas pipelines. The research question is formulated as follows: "Tocomprehend the body of literature and emerging trends on the subjectof risk associated with oil and gas pipelines."2. Defining the list of search sources. The WoS database was selecteddue to its comprehensive coverage of peer-reviewed research in rep-utable journals. In fields like civil engineering and medicine, it is themost widely used database for bibliometric analysis (Xue et al., 2018;Aalirezaei and Kabir, 2021).3. Defining search query based on keywords. To help in searching theWoS database for relevant articles, appropriate keywords are selectedand presented in Table 3.1. The selection of keyword was based on areview of the available literature and papers with similar study. Key-words relating to consequences are partially obtained from Aalirezaeiand Kabir (2021), where the authors conducted a bibliometric litera-ture assessment of the consequence of oil and gas pipeline failure from2000 to 2020. Keywords relating to reliability are partially obtainedfrom Khan et al. (2021), where the authors partially used bibliometrictechnique to find the notable articles and sources in the field of risk-based pipeline integrity management. The keyword that was used inthe search is displayed in Table 3.1, and the search was done on August29, 2022.4. Searching. Based on the query specified in Table 3.1, the search wascarried out, and the WoS displayed 2466 documents. Out of all thedocuments that WoS has extracted, 58.92% (1453) are articles, 37.47%(924) are proceeding papers, 2.88% (71) are review articles, 0.61%(15) are early access documents, and 0.12% (3) are editorial materials.The remaining document types were not included in the analysis thatfollowed, which focused on the articles and review articles. In orderto ensure that majority of the studies relating to the risk assessmentof onshore oil and gas pipelines are taken into account for inclusion inthe final dataset, publications between 1982 and 2022 were selected.5. Assessing quality of results. Although there is no universally accepteddefinition of quality, the quality of the search results is evaluated here inlight of the study’s objectives. The preliminary result is further refinedby excluding articles published in specific WoS categories, such as engi-neering ocean, oceanography, engineering marine, law, nuclear sciencetechnology, telecommunications, zoology, toxicology, statistics prob-333.4. RESULTS AND DISCUSSIONTable 3.1: Search strings used to collect information on publications relatedto risk assessment of oil and gas pipelines.Keyword Search stringRisk (TS = ( ((((oil OR gas) AND ((pipe*) OR (buried pipeline))) OR (pres-surized pipe*)) AND (((risk* OR reliability OR integrity OR (remaininglife) OR safety OR consequence OR hazard OR vulnerability OR (failureprobability)) AND (assessment OR analysis OR estimation)) OR ((fail-ure OR environmental OR financial OR human OR reputation) AND(impact OR loss OR consequence*)))))AND TI=(pipeline AND pipe* ))TS searches for topic terms in the following fields within a record: title, abstract,author keywords, and keywords plus; TI searches the Title field within a record.ability, spectroscopy, astoronomy astrophysics, and other categorieswith two or fewer documents. Some publications may be excludedfrom further analysis (e.g., citation analysis) by utilizing higher criteria(e.g., high number of citation) if they are classified under the categoriesconsidered but are not related to the study’s topic. In order to onlytake into account articles that were published in the English language,a further condition for the language of publication is included.6. Bibliometric analysis of search results. A bibliometric analysis is em-ployed as the main analysis approach since the technique aids in analyz-ing the published literature based on the data that has been reportedwith little author bias and subjective judgment.The bibliometric analysis is carried out using VOSviewer software (Van Eckand Waltman, 2010). It creates a network from bibliometric data that isprovided, and the network is made up of nodes (which stand for documents,sources, authors, organizations, countries, or keywords) and links (representsrelationships among nodes). If there are several occurrences, a node will belarger in size. At the same time, thicker links demonstrate closer connectionsbetween nodes. Additionally, an algorithm is used to create clusters from theclosely linked nodes (Vilutiene et al., 2019; Waltman and Van Eck, 2013).3.4 Results and discussion3.4.1 Trend of researchThe results of the bibliometric search shows the trend of research on thetopic (Figure 3.2). The final database constitute of 1311 articles published343.4. RESULTS AND DISCUSSIONFigure 3.2: Number of articles published per year.between 1982 and 2022. The number of publications produced per yearis depicted in Figure 3.2, and during the last 40 years, there has been anexponential growth. The trend can roughly be divided into three stages:the initial stage (1982–2004), the developing stage (2005–2013), and theprosperous stage (2014-2022). Early in the 2000s, there was a trend towardan increase in the number of articles published on risk assessment of oil andgas pipelines, which may have been influenced by the US’s 2002 PipelineSafety Improvement Act (Khan et al., 2021).During the initial stage, except for 2002, the number of publications werealmost less than ten articles per year. The number of articles published in-creased quickly in the period that followed. The articles published in thisyear increases somewhere between 10 and 40. In the prosperous stage, alot of researchers and institutions began to publish articles about the riskassessment of oil and gas pipelines. With the maximum number of publica-tions in 2021, a similar upward trend in publications has been seen on thebibliometric analysis of oil and gas pipeline failure consequence (Aalirezaeiand Kabir, 2021).353.4. RESULTS AND DISCUSSION3.4.2 Bibliographic couplingWhen two publications share one or more references, they are matchedin a bibliographic coupling since this indicates that the articles have a com-mon intellectual capital (Khanra et al., 2021; Weinberg, 1974). Table 3.2lists the key authors, organizations, and countries that have made signifi-cant contributions to the study of pipeline risk. According to the total linkstrength (TLS), the analyses included 4013 authors, 1297 organizations, and78 countries. TLS evaluates how strongly a researcher or article is currentlylinked to other researchers and articles (Tandon et al., 2021). To ensurethe relevance of the included data, a threshold criterion is used, requiring aminimum of three documents and thirty citations. According to the analysisof the coupling strengths, 48 countries, 158 organizations, and 196 authorsall meet the requirements. Jing Gong is found to be the most influentialauthor in the research topic literature, followed by Weichao Yu and WeiheHuang. Among the organizations, China University of Petroleum, South-west Petroleum University, and University of Western Ontario are found tobe the most influential in the literature. China has the most impact on theliterature related to the research topic, followed by Canada and the UnitedStates.Table 3.2: Bibliographic coupling of contributing authors, countries, andorganizations.SNo. Top 20 authors Top 20 organizations Top 20 countries1 Gong, Jing(9935) China University of Petroleum(19976) China(6462.97)2 Yu, Weichao(9581) Southwest Petroleum University(6815) Canada(4551.7)3 Huang, Weihe(7750) University of Western Ontario (5249) Usa(3749.39)4 Wen, Kai(7280) University of British Columbia(5240) Australia(2111.14)5 Zayed, Tarek(6460) University of Bristol(5107) England(2062.68)6 Li, Yichen(5178) Concordia University(4518) Italy(1879.12)7 Zhou, Wenxing(4777) Macquarie University(4431) Iran(1527.74)8 Khan, Faisal(4508) China National Petroleum Corporation(4420) Malaysia(1205.73)9 Abbassi, Rouzbeh(4432) Universiti Teknologi PETRONAS(3976) France(1077.22)10 Tesfamariam, Solomon(4328) University of Liverpool(3939) Greece(818.56)11 Zhang, Hong(4183) University of Sannio(3925) Netherlands(761.3)12 Liu, Hongfei(3971) Vienna Consulting Engineers Zt Gmbh(3922) Saudi Arabia(754.91)13 Sadiq, Rehan(3798) University of Alberta(3871) India(655.57)14 Zio, Enrico(3498) Delft University of Technology(3791) Japan(642.22)15 Zhang, Jie(3433) University of Calgary(3290) Algeria(570.81)16 Chen, Feng(3374) Memorial University of Newfoundland(3242) Scotland(563.44)17 Wu, Changchun(3374) Hong Kong Polytechnic University(3085) Mexico(559.08)18 Senouci, Ahmed(3114) Aristotle University of Thessaloniki(3058) South Korea(488)19 Zuo, Lili(3079) Xi’an Jiaotong University(3046) Norway(467.59)20 Qin, Guojin(3018) University of Tehran(2918) Qatar(464.12)Note: Threshold criteria: 3 number of documents and 30 number ofminimum citations, TLS indicated in parentheses.363.4. RESULTS AND DISCUSSION3.4.3 Co-occurrence analysis (co-word analysis)The co-occurrence of keywords is measured through co-occurrence anal-ysis, which provides clear insight into the connections between various key-words via nodes (Tandon et al., 2021; Aalirezaei and Kabir, 2021). Thisfacilitates the visualization of publications’ main ideas and the potentialidentification of emerging research trends (Tandon et al., 2021). To com-prehend the study topic addressed by the existing research, a co-occurrenceanalysis using 3848 author keywords was performed. Figure 3.3 shows thenetwork and density visualization of the relationships between the authors’keywords, and Table 3.3 reports the TLS for the top 20 authors’ keywords.Pipeline, natural gas pipeline, and corrosion are identified to be the signif-icant keywords in the network among the top 20 author keywords (Figure3.3). Due to the study’s emphasis on risk evaluation of oil and gas pipelines,this result is expected. However, it seems that natural gas pipeline riskstudies are greater than those for liquid or oil pipelines (Figure 3.3). Thismay be primarily because a natural gas pipeline failure results in fatalitiesor serious human injuries, which are the most critical aspect of risk assess-ment (Brito and de Almeida, 2009). Corrosion appears to be strongly relatedto the causes of pipeline failure, while there are other factors that can im-pair the integrity of pipelines. Additionally, the majority of research on riskassessment focuses on reliability, with the consequences of pipeline failurereceiving less attention. Keywords associated with reliability study are reli-ability, failure pressure, failure analysis and failure probability. Tools suchas finite element method, Bayesian network, and fuzzy logic are found tobe used frequently on this field of research as well. The co-occurence of au-thor’s keywords according to years is shown in Figure 3.4. Among the manytrending keywords, keywords such as machine learning and artificial neuralnetwork indicates the application of these algorithms in oil and gas pipelinestudy for pipeline integrity management, safety assessment, structural healthmonitoring, leakage detection, condition assessment, risk management andothers. On the other hand, the lack of links between important keywordsin the research area, such as GIS and consequence, plus the fact that eachhas smaller nodes, show that there are fewer studies on consequence thanthere are on reliability study. It also shows that there are almost no studythat use GIS software to analyze the consequences of an oil and gas pipelinefailure. There are also fewer articles focusing on financial and environmen-tal consequence assessments and more papers focusing on individual risk inconsequence-related research.373.4. RESULTS AND DISCUSSIONsafety managementparametric analysisimpacthazard analysisfragilitydispersiondefectcorrosion rateaccident analysisleakage detectioninfrastructurefractureflow assuranceexternal corrosionexplosiondecommissioningahpsteel pipelinesprobabilitymetal-loss corrosionjet fireco2bucklingcarbon capture and storagenatural gas pipeline networkmaintenanceintegrity assessmenthydrogen embrittlementhydrogenearthquake stressfailureburst pressurepipeline safetyinspectionpipeline transport safetyoil and gasnumerical simulationfailure analysisrisk analysisfailure pressurepipeline failurecorrosion defectoil pipelinereliabilityoil and gas pipelineburied pipelinenatural gas finite element methodcorrosionnatural gas pipelinepipelineVOSviewer(a)safety managementparametric analysisimpacthazard analysisfragilitydispersiondefectcorrosion rateaccident analysisleakage detectioninfrastructurefractureflow assuranceexternal corrosionexplosiondecommissioningahpsteel pipelinesprobabilitymetal-loss corrosionjet fireco2bucklingcarbon capture and storagenatural gas pipeline networkmaintenanceintegrity assessmenthydrogen embrittlementhydrogenearthquake stressfailureburst pressurepipeline safetyinspectionpipeline transport safetyoil and gasnumerical simulationfailure analysisrisk analysisfailure pressurepipeline failurecorrosion defectoil pipelinereliabilityoil and gas pipelineburied pipelinenatural gas finite element methodcorrosionnatural gas pipelinepipelineVOSviewer(b)Figure 3.3: (a) Network and (b) density diagrams for author keywords. Note:Threshold criteria of minimum 5 keywords.383.4. RESULTS AND DISCUSSIONTable 3.3: TLS for author and index keywords.SNo. Top 20 author keywords TLS1 Pipeline 3142 Natural gas pipeline 1403 Corrosion 1284 Risk assessment 945 Finite element method 826 Reliability 787 Corrosion defect 678 Natural gas 459 Monte carlo simulation 4010 Bayesian network 3911 Failure pressure 3812 Failure probability 3713 Oil and gas pipeline 3714 Fuzzy logic 3415 Oil and gas 3416 Buried pipeline 3117 Inspection 3118 Risk management 3119 Machine learning 2820 Failure analysis 26Note: Threshold criteria: minimum five occurrences.3.4.4 Network analysisCo-authorship and citation analyses are employed in network analy-sis to better understand the structure of scholarly contributions. The co-authorship is used to determine the degree of collaborative research amongauthors, organizations, and countries to advance knowledge in the subjectof study. To highlight the more important collaborations, authors with 3number of documents and thirty citations in the WoS database were cho-sen for the analysis. Thus, the co-authorship network analysis contains only196 authors. The largest collection of connected items, which consists of 90items, is taken into account because some of the 196 items in the networkare not connected to one another. Twelve author collaboration networks,with all clusters connected, are shown in Figure 3.5. Authors with strongrelationships and more articles are set as leading authors in the groups of co-393.4. RESULTS AND DISCUSSIONsafety managementparametric analysisimpacthazard analysisfragilitydispersiondefectcorrosion rateaccident analysisleakage detectioninfrastructurefractureflow assuranceexternal corrosionexplosiondecommissioningahpsteel pipelinesprobabilitymetal-loss corrosionjet fireco2bucklingcarbon capture and storagenatural gas pipeline networkmaintenanceintegrity assessmenthydrogen embrittlementhydrogenearthquake stressfailureburst pressurepipeline safetyinspectionpipeline transport safetyoil and gasnumerical simulationfailure analysisrisk analysisfailure pressurepipeline failurecorrosion defectoil pipelinereliabilityoil and gas pipelineburied pipelinenatural gas finite element methodcorrosionnatural gas pipelinepipelineVOSviewerFigure 3.4: Co-occurence map of authors keywords according to years.authorship analysis. The most active authors, organizations, and countriesthat have high number of articles are presented in Table 3.4.The ranking of authors based on the number of citations will be covered inthe following section. Figure 3.5 and Table 3.4’s findings, however, show thatsome large collaboration networks are responsible for a significant portion ofthe research on the study’s topic. Figures 3.6 and 3.7 display a co-authorshipof organizations and countries generated from the core dataset. A set of 1297organizations and 78 countries are identified by VOSviewer. Out of them,158 organizations and 48 countries meet the requirements of at least 3 articlesand 30 citations. China, Canada, and the United States are the top threenations here. The three top universities in this study are concurrently fromChina and Canada.3.4.5 Citation analysisCitation analysis assesses a publication’s level of acceptance by a quan-titative assessment of other published studies that have cited it. This helpsunderstanding of an article’s influence and popularity, as well as its col-laborative citation network (Tandon et al., 2021). VOSviewer used citationanalysis to comprehend the state of the research on risk analysis of oil and gas403.4. RESULTS AND DISCUSSIONyang, mingsun, fengruiliu, xiongkhakzad, nimazhou, jingmunoz, felipeli, jungaraniya, vikramzuo, liliwang, wenhe li, yichenli, fengcozzani, valeriohu, jinqiuhe, guoxizio, enricolu, hongfangli, xinhongzhang, laibinabbassi, rouzbehtesfamariam, solomonkhan, faisalzayed, tarekliang, yongtugong, jingzhang, hongVOSviewerFigure 3.5: Co-authorship analysis of authors. Note: Threshold crite-ria—minimum of three documents and thirty citations; resolution—1.0; 196of 4013 authors are connected to each other.pipelines in the context of significant authors, organizations, and countries(refer to Table 3.5). The frequently cited authors are Hong Zhang, SolomonTesfamariam, and Wenxing Zhou. China, Canada, and the United States arethree of the top 20 countries that contributed to the most-cited articles inthis discipline. This indicates that authors in these countries may be tacklingcontemporary and globally relevant research topics in this field. The major-ity of the time, the identified institutions are those that have connectionsto highly cited articles in the area, like the China University of Petroleum,Southwest Petroleum University, and University of British Columbia.Citation measures have identified the top 20 popular and well-regardedarticles in this network (refer to Table 3.6). Citations indicate the popularityof articles that pertain to state-of-the-art review of oil and gas pipelines riskassessment.413.4. RESULTS AND DISCUSSIONTable 3.4: The most active authors, organizations and countries that havelargest number of articles focusing on risk assessment of oil and gas pipelines.SNo. Author Articles Organizations Articles Countries Articles1 Zhang, Hong 18 China University of Petroleum 140 China 4822 Zhou, Wenxing 18 Southwest Petroleum University 68 Canada 1563 Gong, Jing 15 University of Western Ontario 27 Usa 1454 Zayed, Tarek 15 University of British Columbia 23 Iran 735 Liang, Yongtu 14 Chinese Academy of Sciences 21 England 726 Khan, Faisal 12 University of Alberta 20 Italy 637 Liu, Xiaoben 12 University of Calgary 17 Australia 468 Tesfamariam, Solomon 12 University of Tehran 16 South Korea 429 Yu, Weichao 12 Universiti Teknologi PETRONAS 16 Russia 3910 Abbassi, Rouzbeh 11 Islamic Azad University 15 Malaysia 3611 Zhang, Laibin 11 Xi’an Jiaotong University 15 Brazil 3512 Zhang, Peng 11 China National Petroleum Corporation 14 Mexico 3113 Cheng, Y. Frank 10 China University of Petroleum (East China) 14 France 2914 Qin, Guojin 10 Concordia University 14 Greece 2615 Li, Yuxing 9 Politecn Milan 14 India 2516 Liang, Wei 9 Tianjin University 14 Poland 2217 Lu, Hongfang 9 Tsinghua University 14 Netherlands 2118 Wen, Kai 9 Dalian University of Technology 13 Algeria 1919 Zhang, Jie 9 Delft University of Technology 13 Nigeria 1820 Huang, Weihe 8 Macquarie University 11 Saudi Arabia 18natl acad sci ukraineeindhoven univ technoluniv wollongongnanjing univuniv sci & technol beijingcolorado state univtexas a&m univcnraristotle univ thessalonikiuniv teknol malaysiamacquarie univpolitecn milanconcordia univuniv tehranuniv albertauniv british columbiauniv western ontariosouthwest petr univchina univ petrVOSviewerFigure 3.6: Co-authorship analysis according to organizations. Note: reso-lution—1.0.423.4. RESULTS AND DISCUSSIONwalesfinlanddenmarkaustriapakistansingaporeqatarscotlandturkeyjapanukrainegermanynigeriaalgeriaindiafrancemexicobrazilmalaysiarussiaaustraliaitalyenglandiranusacanadapeoples r chinaVOSviewerFigure 3.7: Co-authorship analysis according to country. Note: Thresholdcriteria of minimum of three documents and thirty citations, resolution of1.0.Table 3.5: Citation analysis for top 20 authors, organizations, and countries.Author Citation Organizations Citation Country CitationZhang, Hong 398 China University of Petroleum 2062 China 6071Tesfamariam, Solomon 375 Southwest Petroleum University 614 Canada 2589Zhou, Wenxing 369 University of British Columbia 528 Usa 2103Zhang, Laibin 363 University of Western Ontario 433 England 1313Liang, Wei 351 tsinghua univ 433 Italy 1222Sadiq, Rehan 290 korea gas safety corp 346 Australia 961Khan, Faisal 269 china univ petr east china 330 Iran 901Ahammed, M 265 Politecn Milan 318 South Korea 739Ahn, Bj 262 Cnr 315 Brazil 687Jo, Yd 262 Ipn 311 France 603Zayed, Tarek 251 Chinese Academy of Sciences 307 India 598Weng, W. G. 241 Korea Polytech University 285 Mexico 593Abbassi, Rouzbeh 232 University of Alberta 270 Malaysia 374Chen, Guoming 230 University of Tehran 261 Greece 371Li, Xinhong 221 Xi’an Jiaotong University 253 Norway 369Shuai, Jian 215 Memorial University of Newfoundland 229 Netherlands 346Liu, Shuhai 207 Concordia University 224 Turkey 322Gong, Jing 206 Dalian University of Technology 222 Saudi Arabia 240Xu, Kui 201 Macquarie University 218 Algeria 225Zhu, Hongwei 192 University of Waterloo 210 Ukraine 203Note: TLS applied as the ranking measure; threshold criteria: minimumof three document; 30 citations.433.4. RESULTS AND DISCUSSIONTable3.6:Thetop20articleswiththemostcitation.SNo.SelectedpaperDescriptionCitations1parsi(2014)Thisarticlereviewsaboutsolidparticleerosionmodelingforoilandgaspipelines.Anerodedpipelinecanleadtofailure,whichresultsinafinancialandenvironmentallosses.2462ding(2005)Theauthorsproposedafuzzyfaulttreeanalysistoestimatefailureprobabilityofoilandgastransmissionpipelines.Inaddition,theycombinedexpertelicitationwithfuzzysettheoriestoevaluateprobabilityofevents.Thismethodisusefultotreatfuzzyeventsofafaulttreeanalysis.1953datta(2016)Thepaperdiscussesaboutthemeritanddemeritsofvariouspipelinefaultdetectionmethods,includingVibrationanalysis,Pulseechomethodology,Acoustictechniques,Negativepressurewavebasedleakdetectionsystem,SupportVectorMachinebasedpipelineleakagedetection,Interferometricfibresensorbasedleakdetection,FilterDiagonalizationMethod(FDM),andothers.187Continuedonnextpage443.4. RESULTS AND DISCUSSIONTable3.6:Thetop20articleswiththemostcitation.(Continued)SNo.SelectedpaperDescriptionCitations4caleyo(2002)Theauthorsproposedaprobabilisticmethodologyforestimatingtheremaininglifeofpressurizedpipelinescontainingactivecorrosion.Thefollowingapproacheswereusedtoestimatethefailureprobabilityassociatedwithcorrosiondefectovertime:first-ordersecond-momentiterativereliabilitymethod,MonteCarlointegrationandfirst-orderTaylorseriesexpansionofthelimitstatefunction.1735choi(2003)Theauthorsproposedafitness-for-purposetypelimitloadsolutionforassessmentofcorrosiondefectsinAPIX65gaspipelinesbycomparingexperimentaldatawithafiniteelementanalysesresults.1646shahriar(2012)Theauthorsdevelopedagenericframeworktoevaluateriskofnaturalgaspipelinefailurethatcanbothhandledataandmodeluncertaintiesinriskanalysis.Abow-tiemethodisusedtointegrateafaulttreeandeventtreetorepresentcausesandconsequenceofaneventinacommonplatform.159Continuedonnextpage453.4. RESULTS AND DISCUSSIONTable3.6:Thetop20articleswiththemostcitation.(Continued)SNo.SelectedpaperDescriptionCitations7li(2016)Thepaperproposedacoupledbow-tieandBayesiannetworkinriskanalysisofsubmarineoilandgaspipelines.Bow-tieisusedforhazardidentificationandmodelingoftheescalationprocessofpipelineleakage.1468brito(2009)Theauthorsproposedamulti-attributeutilitytheoryforriskassessmentandriskrankingofnaturalgaspipelinesections.Threedimensionsofconsequencesnamelyhuman,environmental,andfinancialareconsideredtorepresenttheoutcomesofpipelinefailure.1359ahammed(1998)Thepaperdiscussesaboutaprobabilisticapproachtoestimatetheremaininglifeofapipelineinthepresenceofactivecorrosiondefects.134Continuedonnextpage463.4. RESULTS AND DISCUSSIONTable3.6:Thetop20articleswiththemostcitation.(Continued)SNo.SelectedpaperDescriptionCitations10han(2011)Thestudydevelopedaqualitativeandquantitativeriskassessmentmethodsforurbannaturalgaspipelinenetwork.Themethodsareappliedtotwourbannaturalgaspipelinenetworks,andtheoutcomeindicatedtheirapplicabilityforpracticalapplications.Thechoicebetweenthetwomethodsdependsonavailabilityofdataandprecisionrequirementsofriskassessment.12511jo(2005)Theauthorsproposedasimplifiedquantitativeriskassessmentmethodfornaturalgaspipelines.Fatallengthandcumulativefatallengthparametersareintroducedinthestudy,whichcanbeobtainedfrompipelinegeometryinformationandpopulationdensityofaGIS,toindicatetheintegratedfatalityalongthepipelineassociatedwithanaccidentsandsectionofapipelineinwhichanaccidentleadstoNormorefatalities,respectively.120Continuedonnextpage473.4. RESULTS AND DISCUSSIONTable3.6:Thetop20articleswiththemostcitation.(Continued)SNo.SelectedpaperDescriptionCitations12velazquez(2009)Theauthorsdevelopedamodelforpredictionofmaximumpittingdepthasafunctionofexposuretime,soilandpipecharacteristics.Themodelparametersareestimatedasalinearcombinationofsoilandpipecharacteristics,underaconstraintthateachpredictorvariablecouldbeassociatedwithonlyoneparameter.11813brito(2010)Thepaperproposedariskassessmentmethodofnaturalgaspipelinesforclassifyingsectionsofpipelineintoriskcategoriesusingamulticriteriamodel.ThemodelintegratesaUtilityTheoryandtheELECTRETRImethod.Theriskassessmentmethodconsidersthehuman,environmentalandfinancialimpactsduetopipelineaccident.11514ma(2013a)Thepaperinvestigatesthefailurepressureoilandgaspipelineswithorwithoutcorrosiondefects.Afailurepressurepredictionmodelisproposedandvalidatedusing79groupsoffull-scalebursttestdata.111Continuedonnextpage483.4. RESULTS AND DISCUSSIONTable3.6:Thetop20articleswiththemostcitation.(Continued)SNo.SelectedpaperDescriptionCitations15capelle(2008)TheauthorshaveperformedabursttestonnotchedX52pipesafterbeingincontactwithhydrogeneitherfrominsidethepipeoroutside.11116jamshidi(2013)Thestudyproposedafuzzylogicmodelforpipelineriskassessment.Comparisonbetweentheproposedmodelandclassicalriskassessmentapproachismade,andtheoutcomeindicatesthattheproposedmodelprovidesanaccurateresults.10617adegboye(2019)Thepaperdiscussesaboutrecentadvancesinpipelinemonitoringandoilleakagedetectiontechnologies.10418sharma(2017)Thereviewpaperfocusesonweldingofhighstrengthoilandgaspipeline,whichisusefultobearinternalfluidpressureandadverseexternalconditions.Therequirementsofchemicalcompositionandmechanicalpropertiesforweldingofhighstrengthsteelsarediscussedindetailtounderstandtheissuesrelatedwiththesematerials.102Continuedonnextpage493.4. RESULTS AND DISCUSSIONTable3.6:Thetop20articleswiththemostcitation.(Continued)SNo.SelectedpaperDescriptionCitations19jin(2014)Thestudyproposedanintegratedleakagedetectionandlocalizationmodelforgaspipelinebasedonacousticwavemethod.9920lu(2015)Thestudyproposedacomprehensiveriskevaluationmethodbycombiningariskmatrixwithabow-tiemodel.Itaddressthelimitationofpreviousstudiesonthebow-tiemodel,lackofquantitativeconclusions,byincludingaquantitativeriskmatrixthatrankstheprobabilitycriteriaandconsequenceseveritycriteria.97503.5. SUMMARYTable 3.7: Journal ranking according to citation analysis.SNo. Source Number of articles Number of citations TLS1 Journal of Loss Prevention in the Process Industries 75 2349 6102 Engineering Failure Analysis 78 1482 3823 International Journal of Pressure Vessels and Piping 65 1425 3174 Journal of Natural Gas Science and Engineering 59 1379 3945 Reliability Engineering & System Safety 40 798 3906 Process Safety and Environmental Protection 42 774 3157 Journal of Hazardous Materials 12 539 1588 Applied Energy 9 356 599 International Journal of Hydrogen Energy 15 334 4410 Structure and Infrastructure Engineering 16 302 12811 Sensors 9 267 4512 International Journal of Greenhouse Gas Control 15 265 2513 Journal of Pressure Vessel Technology-Transactions of the ASME 37 263 12214 Corrosion 6 210 5115 Tunnelling and Underground Space Technology 8 194 6316 Journal of Petroleum Science and Engineering 13 170 4517 Soil Dynamics and Earthquake Engineering 11 165 4318 Journal of Cleaner Production 9 162 7819 Journal of Pipeline Systems Engineering and Practice 38 162 15120 Energy 11 161 52Note: Threshold criteria: minimum 3 articles and 30 citations.Based on the source, citation analysis shows the influence of the jour-nals on the research topic. According to the ranking of citations, Table 3.7lists the top journals that contributed to this study. A threshold criteriaof minimum three papers and 30 number of citations per source has beenused to filter the most important sources. Additionally, Figure 3.8 displays anetwork visualization of the journals that illustrates the cross-citation mapbetween them. Journal of Loss Prevention in the Process Industries, Engi-neering Failure Analysis, and International Journal of Pressure Vessels andPiping are the top journals in this field.3.5 SummaryA bibliometric analysis is carried out in the current study to highlight thescientific studies related to risk assessment of oil and gas pipelines. In orderto study the published papers associated with the subject topic, many meth-ods including trend analysis, bibliographic coupling, co-occurrence analysis,network analysis, and citation analysis are used. The published papers areextracted from WoS database using failure and consequence related key-words that are obtained from previous study. Using several criteria, theinitial search result is further filtered, and the final list of results comprises1311 papers that were published between 1982 and 2022. Subsequent dataanalysis is conducted based on these articles. Overall, the number of pub-lished papers showed an increasing trend in the past 40 years. According to513.5. SUMMARYenvironmental earth sciencesenergy & fuelselectric power systems researcearthquake spectraplos onesustainabilitynatural hazardssensorsenergy policyjournal of petroleum science ainternational journal of hydrointernational journal of greenstructure and infrastructure ejournal of natural gas scienceinternational journal of pressjournal of loss prevention in engineering failure analysisVOSviewerFigure 3.8: Citation analysis according to sources. Note: Threshold criteriaof minimum of three documents and thirty citations, resolution of 1.0.the keyword analysis, reliability analysis is a topic covered in more studiesthan consequence analysis. Additionally, research focused on natural gaspipeline as opposed to oil pipeline are more prevalent. According to the net-work analysis of the current study, China, Canada, and the United Statesare leading this field of study. The top research institutions in this area,including China University of Petroleum, Southwest Petroleum University,University of Western Ontario, and University of British Columbia, are con-currently located in these nations.The co-occurence study demonstrates unequivocally that the failure anal-ysis of an oil and gas pipeline is more thoroughly examined than the analysisof its consequences. Additionally, it was difficult to find research with sig-523.5. SUMMARYnificant recognition that studied the consequences of pipeline failure usingGIS to identify areas with high consequences, which is essential for pipelineoperators to allocate resources for monitoring. So, the current work makesa significant contribution to improving our understanding of consequenceassessment in future research. The current study can be expanded in the fu-ture by include a thorough literature review, comparing results by includingrelevant keywords, and changing the threshold criteria used in each analysis.53Chapter 4Handling incomplete andmissing data in corrosion pitmeasurement database usingimputation methods4.1 BackgroundData scarcity and missing values are a prime challenge in developing acorrosion prediction model. In this paper, eight imputation techniques areexplored using the National Bureau of Standards (NBS) corrosion database.The eight imputation techniques are: mean, median, linear regression (LR),K-nearest neighbour (KNN), iterative robust model-based imputation (IRMI),multiple imputations of incomplete multivariate data (AMELIA), sequentialimputation for missing values (IMPSEQ) and principal component analysis(PCA). The utility of imputation techniques is checked by training a neuralnetwork (NN) on the datasets imputed by the eight techniques. Among thetechniques, KNN and IMPSEQ performed better by achieving a low errorand high coefficient of determination R2. Results were compared with abaseline accuracy, where the NN model was trained on the original corrosiondataset without the missing values. The NN performance increased from thebaseline accuracy (81%) when it was trained by KNN (85%) and IMPSEQ(91%) imputed datasets.4.2 Data descriptionThe corrosion database is obtained from the studies conducted by NBSbetween 1922-1940 to analyze the corrosion in bare steel and wrought ironpipelines (Romanoff, 1957; Ricker, 2010). The database was collected from47 experimental sites in the US and carried out by burying eight differentpipe samples and exposed to different types of soils. The samples have been544.2. DATA DESCRIPTIONretrieved periodically, cleaned and the average mass change and deepestpenetration into the wall of pipes was assessed. They were identified bysingle letter (a, b, e, y, B,K,M, Y ) to indicate their size (1.5 in and 3 in)and alloy composition (Romanoff, 1957). The pit depth of all the eightsamples are considered in this study in order to use the maximum informationavailable. The soil texture for the 47 sites are categorized by plotting overthe ternary diagram (Fig. 4.1) generated using a Matlab script preparedby Holger Hoffmann (2021). Majority of the soil samples contain clay, siltand loam or their composition.Clay [%] Silt [%]Sand [%]Sand Loamy sandSandy LoamLoamSilt LoamSiltSandy Clay LoamClay Loam SiltyClay LoamSandy ClayClaySilty ClayFigure 4.1: Plot of the measured soil samples overlaid on the ternary dia-gram.The factors considered in pit depth prediction are exposure time, inter-nal drainage, diameter, depth, sand, silt, clay, colloid, suspension, resistiv-ity, temperature, precipitation, moisture equivalent, air-pore space, specificgravity, volume shrinkage and pH. A pairwise correlation of the continu-ous variables is shown in Fig. 4.2, where a value of +1 for the Pearson’scorrelation indicates total positive correlation, and -1 indicates total nega-tive correlation. Among these factors, the variables with missing data aresand, silt, clay, colloid, suspension, air-pore space, specific gravity and vol-ume shrinkage as shown on the left side of Fig. 4.3. Statistical summary ofthe variables with missing values is summarized in Table 4.1. A boxplot is554.2. DATA DESCRIPTION1.0 -0.23 -0.092 -0.15 0.14 0.045 0.085 0.047 -0.10 0.21 0.19 0.20 -0.11 0.029 0.050 -0.151.0 -0.0180.032-0.0790.031 0.020-0.0340.064 0.018 0.023-0.0110.034-0.0150.00310.0141.0 -0.12 0.18 -0.016-0.081-0.038 -0.18 -0.53 -0.42 -0.0280.016 -0.23 -0.12 0.511.0 -0.52 -0.68 -0.65 -0.55 0.56 -0.094 0.16 -0.79 0.74 -0.25 -0.53 -0.291.0 -0.28 -0.29 0.35 -0.36 -0.22 -0.27 0.031 -0.29 -0.19 -0.25 0.181.0 0.98 0.31 -0.32 0.29 0.048 0.86 -0.59 0.45 0.80 0.171.0 0.23 -0.29 0.38 0.11 0.89 -0.55 0.44 0.84 0.0751.0 -0.50 -0.043 -0.33 0.26 -0.60 0.43 0.30 0.281.0 0.037 0.26 -0.40 0.75 -0.39 -0.36 -0.341.0 0.57 0.37 -0.20 0.31 0.32 -0.241.0 0.039 0.11 -0.089-0.040 -0.661.0 -0.62 0.40 0.82 0.171.0 -0.62 -0.49 -0.251.0 0.59 0.301.0 0.211.0Pit depthExposure timeBurial DepthSandSiltClayColloidSuspensionResistivityTemperaturePrecipitationMoistureAir poreSpecific GravityVolume ShrinkagepHPit depthExposure timeBurial DepthSandSiltClayColloidSuspensionResistivityTemperaturePrecipitationMoistureAir poreSpecific GravityVolume ShrinkagepH-1-0.8-0.6-0.4-0.200.20.40.60.81Figure 4.2: Pairwise correlation of the continuous variables in the NBS data.also provided in Fig. 4.4 to assess the variables distribution. The variableshave fair data distribution, with few extreme observations observed in silt,air-pore space and volume shrinkage. Extreme observation or outlier is anyvalue that is either greater than or less than the upper or lower limits ofthe boxplot edge, respectively. As a result, each record that corresponds toan outlier is removed from the dataset to prevent bias in further analysis.Compared to the size of the dataset, the removed records are very few. Themissing rate for volume shrinkage and air-pore space is below 5%, whereas itis above 10% for the remaining variables. Widaman (2006) has given a guidefor handling missing data in many area of applications. The study classifiedmissing rate as negligible (1%-2%), minor (5%-10%), moderate (10% - 25%),high (25% - 50%) and excessive (> 50%). Based on this assumption, the dif-ferent imputation methods are applied to the variables which have minor tohigh missing rate.564.3. METHODOLOGYPit.depthExpTimeDrainageDiameterDepthSand SiltClayColloidSusp.ResistivityTemp.Precip.MoistureAir.poreSG VS pH0500100015002000Index(a) Original datasetPit.depthExpTimeDrainageDiameterDepthSand SiltClayColloidSusp.ResistivityTemp.Precip.MoistureAir.poreSG VS pH0200400600800100012001400Index(b) Test datasetFigure 4.3: Corrosion datasets with their observed (grey) and missing values(red)4.3 MethodologyThe main objective of this study is to handle missing values in an in-complete corrosion database by using different imputation techniques. Aflowchart of the proposed methodology is shown in Fig. 4.5. The proposedmethodology has comparison of imputation techniques and evaluating im-portance of imputation techniques using a neural network model.The eight imputation techniques (mean, median, LR, KNN, IRMI, PCA,AMELIA, and IMPSEQ), implemented in RStudio (RStudio Team, 2020),were first used on complete corrosion test dataset. This test dataset is usedto examine the effectiveness of different imputation techniques on valuesthat have been intentionally removed. The complete database was initiallygenerated by removing rows with a missing value from the original corro-sion dataset. Following the missing values pattern in the original corrosiondataset (Fig. 4.3), some observed values in the test dataset were deleted toreproduce the missing values pattern in the original dataset. Similarity of themissing values pattern for the original corrosion dataset and test dataset can574.3. METHODOLOGYTable 4.1: Statistical summary of the variables with missing data.Parameters Mean Std Min 25(%) 50(%) 75(%) Max Missing Missing (%)Sand 28.13 27.01 1.2 3.5 20.3 42.1 97.3 13 27.66Silt 37.85 18.59 2.1 24.3 37.8 48.8 78.4 13 27.66Clay 34.01 20.68 0.6 17.625 33.3 46.1 87.8 13 27.66Colloid 27.07 17.16 2.7 14.8 26.1 37.8 71.5 14 29.79Suspension 22.75 11.59 1.8 16.025 22.15 28.2 45.5 13 27.66Air pore 14.26 11.15 1.1 5.7 10.9 18.7 40.6 2 4.26Specific Gravity 1729.19 182.57 1260 1600 1720 1850 2080 10 21.28Volume Shrinkage 7.46 9.69 0 0.2 4.3 8.2 39.8 1 2.131Sand0501001Silt0204060801Clay0204060801Colloid02040601Suspension0102030401Air-pore space0102030401Specific Gravity14001600180020001Volume Shrinkage010203040Figure 4.4: Boxplot of soil variables that have missing values.584.3. METHODOLOGYNBS original corrosion dataset collected between 1922-1940Create test dataset by randomly deleting some of the observed values in the NBS original corrosion dataset. The test dataset has a missing value of 30%Pit depthCorrosion factorsNeural network to model the corrosion pit depthApply the imputation techniques on the NBS original corrosion dataset and use the new imputed datasets to train a NN modelApply sensitivity analysisCompare the estimation performance of eight imputation methods(Mean, Median, LR, KNN, IRMI, AMELIA, IMPSEQ and PCA) on the test datasetFigure 4.5: Flowchart of proposed methodology.be observed respectively, on the left and right side of Fig. 4.3. The imputa-tion techniques were used to estimate the missing values in the test dataset.This analysis, indeed, will show utility of the imputation technique, as theresults will be compared with a known (intentionally removed) parameter.The next part of the investigation entails utility of the imputation tech-nique in completing the original corrosion dataset, and using the eight im-puted datasets on the corrosion pit depth prediction. The corrosion pitdepth prediction was carried out using NN model. The model performanceon the original corrosion dataset after applying deletion technique is takenas a baseline model accuracy. After the imputation, each dataset was di-vided into 70% training and 30% testing datasets. The model parameters(e.g., weights of connections between neurons) were calculated by trainingthe model with the training dataset. The test dataset has not been used dur-ing this period and used only to give unbiased evaluation on the model fit.Details of the different imputation techniques and NN model are explainedin the following subsections.4.3.1 Imputation methodsMean and median imputationThe mean and median imputation methods fill the missing values of avariable with an average and median value of an observations, respectively.594.3. METHODOLOGYMedian imputation is preferred over mean imputation when the variable hasa skewed distribution (McKnight et al., 2007). Although both techniquesare easy to apply, but they tend to reduce the standard deviation of theestimates, ignore the correlation between features and often leads to poorimputation (Little, 1988). They are mathematically presented as follows:Yˆ =1nn∑i=1yobs (4.1)Yˆ = f(yobs) (4.2)where Yˆ and yobs are imputed single value and observed values in a data,respectively; f() is a function for computing the median of an observation.Linear Regression (LR)A LR imputation is a statistical tool which is used to predict a missingvalue based on the relationship between an input and output variables. Adrawback of this technique is that bias is introduced because the imputeddata does not have an error term considered during estimation. This leadsto low variability (Osman et al., 2018). Moreover, the imputed data willhave high correlation with the observed data, which may not be the casealways. Hence, the technique results in overestimating the correlation inthe subsequent analysis (Gao et al., 2018). The regression based imputationprocess can be summarized in the following steps:1. Estimate the regression coefficients that relate target variable to the nexplanatory variables which have high correlation (e.g.,X1, X2, ..., Xn).This estimation is based on subset of a complete data.2. The missing values of a target variable are calculated using the es-timated regression coefficients. The regression equation is written asfollows:Yˆ = βˆ0 + βˆ1X1 + βˆ2X2 + ...+ βˆnXn (4.3)The R simputation package is used in this study to perform the LR im-putation, which imputes numerical variables based on predictors (Van derLoo, 2021). The LR formulae has to be defined as part of the model input,with the left and right hand side of the formula indicating the variable to beimputed and predictor variables, respectively. In this study, the predictorvariables that have a high correlation (greater than 0.5) with the predicted604.3. METHODOLOGYvariable are taken for estimating the missing value. During training to esti-mate the model coefficients, the method removes cases with missing valuesin the training data for both predicted and predictor variables. Finally, themethod estimates the predicted variable’s missing values using the modelcoefficients. If a predictor variable has a missing value during imputation, itis assumed to be zero, which will impact the model result.K-nearest neighbor (KNN)KNN is a machine learning algorithm, which can be used to estimatemissing values of a categorical or numerical attributes. For a given variablewith missing values, the KNN algorithm is designed to find K nearest neigh-bors from all complete observations in a given dataset and then replace themissing value based on either majority or mean rule (Zhang, 2012). Let theset of K nearest neighbors of Y are represented by v = {vk}Kk=1, in whichthe optimal value of K is find out using cross validation Jerez et al. (2010).For a discrete variable, the jth missing value in Y is estimated using themode of v = {vk}Kk=1. For a continuous variable, there are different typesof estimation procedures that can be implemented in KNN (e.g., mean andweighted mean estimation) (Jerez et al., 2010).− Mean estimation: The missing value of yj is replaced by the mean ofthe K nearest neighbors as:yj =1KK∑k=1vkj (4.4)− Weighted mean estimation: The contribution of each K neighbors isweighted (wk) by their distance to yj and this gives more weight to thecloser neighbors:yj =1KK∑k=1wk vkj (4.5)The R V IM package is used in this study to perform the KNN imputa-tion, which uses the aggregation of the nearestK neighbors value to estimatea missing value (Templ et al., 2022). It involves two steps: 1) computing thedistance for defining the closest neighbors using an extension of the Gowerdistance, and 2) combining the K values into a single imputed value. Themedian is a default function used to aggregate the K values in case of anumerical variable. In this study, the K value is set to a default value of 10.614.3. METHODOLOGYUsers can also specify the vector of variables used to calculate the distancesas part of the model input. The algorithm settings are left at their defaultvalues because there are many variables in the current study with a lot ofmissing values, including selecting all the variables for distance computation,not assigning weight for each distance variable, and using an imputed valuefor distance calculation when imputing another variable.Iterative robust model-based imputation (IRMI)IRMI is a model-based imputation method where the missing values areestimated by a sequence of regression models (Templ et al., 2011). Through-out the process, one variable is taken as response and the remaining asregressors to impute the missing values. The procedure of this iterativealgorithm is well summarized by Templ et al. (2011) as follows :1. Initialize the missing values using any of the above-mentioned simpleimputation techniques (median imputation is used by default)2. Sort the variables based on the number of missing values, M(x1) ≥M(x2) ≥ . . . ≥ M(xp), where M(xj) denotes the number of missingvalues in variable xj . Denote I = 1,2,. . . ,p3. Set I=14. Denote ml = 1, . . . , n the indices of the observations that were origi-nally missing in the variable xl, and ol = 1, . . . , n\ml the indices corre-sponding to the observed cells of xl. Separate the observed and missingvalues into two matrices, XolI\{l} and XmlI\{l} respectively. The first col-umn of XolI\{l} and XmlI\{l} consists of ones to handle the intercept termin the regression problem with unknown regression coefficients β andan error term εxoll = XolI\{l}β + ε, (4.6)5. Estimate the regression coefficients βˆ using Eq. (4.6), and use βˆ toestimate the missing valuesxˆmll = Xmlk βˆ, (4.7)6. Carry out Steps 4-5 in turn for each l = 2,. . . ,p.7. Repeat Steps 3-6 until the imputed values stabilize624.3. METHODOLOGYIn this study, the IRMI imputation is carried out using the R V IMpackage, where one variable is used as a response variable in each iterationand the other variables act as regressors (Templ et al., 2022). In this study,missing variables are initially initialized with the median value, and theiterative procedure is then continued until the algorithm converges. Thealgorithm is very useful to get a reliable result in an automated manner. Ifusers are familiar with the relationships between the dataset’s variables, theymay additionally define the model formulas for each variable. No formulasare specified in the current study.Multiple imputations of incomplete multivariate data (AMELIA)The AMELIA algorithm uses an expectation-maximization with a boot-strapping (EMB) to estimate missing values in a dataset (Honaker et al.,2011). This technique considers that the complete dataset has multivari-ate normal distribution and the data are missing at random. The stepsfollowed to apply AMELIA is shown below, but interested readers are re-ferred to Honaker and King (2010) and Honaker et al. (2011) for a detaildescription.1. Separate the datasetD into observed (Dobs) and missing (Dmis) values.The missing values are estimated using a Bayesian concept as:ρ(Dobs|δ) =∫ρ(D|δ)dDmis (4.8)where δ = (µ,Σ) = distribution parameter with mean vector µ andcovariance vector Σ2. Define a matrix M that has one and zero elements, which representthe observed and missing values, respectively.3. Create m samples from M using the bootstrapping4. Estimate the posterior δ for the m samples using the EMB algorithm5. Estimate the missing values in each m sample using the conditionaldistribution of Dobs and posterior parameter6. Take the average of m estimates to get each missing valueThe R Amelia package is used in this study to perform the AMELIAimputation using bootstrap EM algorithm (Honaker et al., 2022). The out-put of the functions are m imputed datasets with no missing values. The634.3. METHODOLOGYpackage settings are left at their default values, and there are 5 datasets thatare imputed in the current study. Average of the imputed datasets is takento combine the 5 imputed values into single value.Sequential imputation for missing values (IMPSEQ)The IMPSEQ algorithm proposed by Verboven et al. (2007) is a covariance-based imputation method. This technique estimates the missing values byminimizing the determinant of the covariance matrix of the data. The stepsfollowed to apply IMPSEQ imputation is shown below, but interested readersare referred to Verboven et al. (2007) for a detail description.1. Partition the dataset into complete and missing matrices, each repre-sented by Xcom and Xmis, respectively:2. Arrange the variables based on the increasing number of missing values,Xmis3. For a variable with the least number of missing values, estimate themissing values by minimizing the determinant of the covariance matrixwith respect to Xmis, wherecov(Xmis) =t− 1tcov(Xcom)+1t(Xmis−Xcom)(Xmis−Xcom)′ (4.9)t is the total number of observed values and Xcom is the mean ofobserved values4. Update the missing values in Xcom5. Repeat Steps 2 to 4 for the remaining variables.The R rrcovNA package is used to carry out the IMPSEQ imputation inthis study (Todorov, 2020). The algorithm starts with a complete subset ofthe data and sequentially estimates the missing values in an incomplete ob-servation by minimizing the determinant of the covariance of the augmenteddata matrix. Because the function is vulnerable to the influence of outliers,all outliers are removed from the corrosion dataset.Principal component analysis (PCA)Principal component analysis (PCA) is a data analysis technique thatis used to compress data sets of high dimensional vectors into lower dimen-sional ones (Ilin and Raiko, 2010). However, the technique aims to retain644.3. METHODOLOGYthe maximum amount of variance in the dataset. This is achieved by findingmutually orthogonal directions of uncorrelated variables known as princi-pal components. The first few components retains most of the variationspresent in the original dataset Osman et al. (2018). The interested readersare referred to Josse et al. (2011) for handling missing values in PCA.The R missMDA package is used to carry out the PCA imputation inthis study (Husson and Josse, 2020), where it uses an iterative PCA algo-rithm or the regularised iterative PCA algorithm. It involves two steps: 1)computing the missing values with initial values (e.g., mean) or values drawnfrom a gaussian distribution, and 2) imputing the missing values with the(regularized) reconstruction formulae of order equal to the number of prin-cipal components (the fitted matrix computed with the number of principalcomponents for the (regularized) scores and loadings). The PCA-based pa-rameter estimates and (regularized) fitted matrix-based imputation of miss-ing data are iterated until convergence (Husson and Josse, 2020). Becauseit is time consuming to consider more than 2 components, the number ofprincipal components in this study is set to a default value of 2.4.3.2 Neural network (NN)Neural network model is a ML algorithm built upon the inspiration ofthe biological information processing system. It has an input, hidden andoutput layer. The layers are composed of an interconnected group of neu-rons that can perform nonlinear mapping between the input and outputvectors (Bishop, 1995). The simplest NN architecture used in many fields ofengineering is the multilayer perceptron (MLP). Increase of the MLP hiddenlayers results in increase of the network’s ability to generalize and level of ab-straction (Bengio, 2009). Mathematically, the equation of a NN is expressedas following:y = f(∑i=1wi xi + b)(4.10)where y is output of the model, f(·) is activation function, wi is weight cor-responding to input xi, and b is bias. The activation functions (e.g., sigmoid,Tanh and ReLU) perform nonlinear transfer to the weighted sum of all theinputs in a neuron. The ReLU activation function is often used becausethey are faster and do not suffer from a vanishing gradient problem (Sharmaet al., 2017).During model training, the connection weights and biases of NN areoptimized until the whole network develops an internal representation whichcaptures the underlying relations between the input and output variables.654.3. METHODOLOGYThe most popular learning algorithm for MLP is backpropagation algorithm,which minimize the error between model output and target value (Hassoun,1995). In a regression problem, Euclidean loss function is used to measurethe prediction error of the model. Mathematically, it’s written as follows:EMSE (X,Y ) =12mm∑i=1(yi − yˆi)2 (4.11)where EMSE is mean squared error; yi and yˆi are set of observed output andmodel predicted, respectively, corresponding to the set of inputs X.4.3.3 Performance evaluationThe performance of the imputation techniques were assessed using coef-ficient of determination (R2) and error indices , such as root mean squareerror (RMSE), mean absolute error (MAE), percent bias (PBIAS), the mean(µ) and a20− index. Mathematically they are expressed using the followingequations:R2 = 1−∑Ni=1(Yi − Yˆi)2∑Ni=1(Yi − Y¯i)2(4.12)RMSE =√∑Ni=1(Yi − Yˆi)2N(4.13)MAE =∑Ni=1|Yi − Yˆi|N(4.14)PBIAS =∑Ni=1(Yi − Yˆi)∑Ni=1(Yi)× 100 (4.15)µ =1NN∑i=1(YˆiYi)(4.16)a20− index = m20N(4.17)where Yi is observed value, Yˆi is predicted value, Y¯i is mean of the obser-vations, and m20 is number of results where the predicted to the measuredpit depth is between 0.8 and 1.2.664.4. RESULT AND DISCUSSION4.4 Result and discussionThe estimation performance of the imputation techniques are presentedin Figs. 4.6 and 4.7, and Table 4.2. Fig. 4.6 indicates the result for the mean,median, LR, and KNN imputation methods, and Fig.4.7 indicates the resultfor IRMI, IMPSEQ, AMELIA and PCA imputation methods. Mean andmedian techniques have a single imputed value for a corresponding observedvalues. Both techniques can underestimate the variance of the missing valuesand lead to a greater bias when the percentage of missing data is high. Thelinear regression estimates a missing value of a target variable based on theobserved values of the explanatory variables that have high correlation withthe target variable. This is obtained from the pairwise correlation plot inFig. 4.2. For instance, the missing values in the suspension variable are cal-culated based on the observed values of all the variables that have a strongcorrelation with suspension (such as sand, resistivity, air-pore space). TheRMSE and MAE of the predicted suspension values are 9.43 and 7.16, re-spectively, when sand alone is used as the explanatory variable. The RMSEand MAE of the predicted suspension values are 8.63 and 6.59, respectively,when sand, resistivity, and air-pore space are used as the explanatory vari-ables. The LR based imputation technique perform good if all the highlycorrelated explanatory variables are included in the analysis.The R2 for all the variables indicated that mean and median methodsperformed the worst of all the methods. Missing values estimated by LR andKNN showed a better performance because the observed and imputed valuesare distributed along the ideal line (the line along which the observed andestimated values are equal). The LR ability to give good estimation dependson the relationship between the explanatory and target variables. A poorlyfitted regression model can impact the estimated missing values, e.g., siltvariable has the lowest R2 and the estimated values are not well distributedalong the ideal line (Fig. 4.6). The KNN algorithm showed high performancein estimating missing values of all the variables and this is can be seen inFig. 4.6 and Table 4.2, where the imputed values are distributed along theideal line and high R2 achieved, respectively. Furthermore, the multipleimputation techniques (IRMI, IMPSEQ and AMELIA) have shown goodperformance as indicated in Fig.4.7 and Table 4.2. The R2 of the multipleimputation methods are higher than the simple imputation methods andPCA. Overall, the highest R2 were acheived by KNN and IMPSEQ.The performance metrics calculated using Equations 4.13 to 4.17 are pre-sented in Table 4.2. For sand, both RMSE and MAE indicated that medianand mean imputation methods performed the worst, followed by PCA and674.4. RESULT AND DISCUSSION0 20 40 60 80 100Imputed Sand 020406080100Observed Sand MeanMedianLRKNN0 20 40 60 80 100Imputed silt 020406080100Observed silt MeanMedianLRKNN0 20 40 60 80 100Imputed clay 020406080100Observed clay MeanMedianLRKNN0 20 40 60 80 100Imputed colloid 020406080100Observed colloid MeanMedianLRKNN0 20 40 60 80 100Imputed Suspension 020406080100Observed Suspension MeanMedianLRKNN1200 1400 1600 1800 2000Imputed SG 12001400160018002000Observed SG MeanMedianLRKNNFigure 4.6: Comparison of observed and imputed values of sand, silt, clay,colloid, suspension, and specific gravity. The symbols red cross, black di-amond, blue cross, and gree circle represent mean, median, LR, and KNNmputation methods.LR. The multiple imputation methods outperformed the single imputationmethods and the best methods were found to be KNN and IMPSEQ. ThePBIAS indicated that almost all methods (except AMELIA, IMPSEQ andPCA) overestimate the observed values, with IRMI giving the least percentbias and AMELIA imputation method placing second. The RMSE and MAEfor silt variable indicated that single imputation methods and PCA didn’tperform well. However, similar to sand, both KNN and IMPSEQ showedgood performance in estimating the missing values of silt variable. ThePBIAS indicated that PCA underestimated the observed values and has aleast percent bias out of all the methods. The KNN, IMPSEQ and AMELIAmethods performed well in estimating the missing values of clay variable. Inaddition, the PBIAS results indicated that all the methods underestimated684.4. RESULT AND DISCUSSION0 20 40 60 80 100Imputed Sand 020406080100Observed Sand IRMIIMPSEQAMELIAPCA0 20 40 60 80 100Imputed silt 020406080100Observed silt IRMIIMPSEQAMELIAPCA0 20 40 60 80 100Imputed clay 020406080100Observed clay IRMIIMPSEQAMELIAPCA0 20 40 60 80 100Imputed colloid 020406080100Observed colloid IRMIIMPSEQAMELIAPCA0 20 40 60 80 100Imputed Suspension 020406080100Observed Suspension IRMIIMPSEQAMELIAPCA1200 1400 1600 1800 2000Imputed SG 12001400160018002000Observed SG IRMIIMPSEQAMELIAPCAFigure 4.7: Comparison of observed and imputed values of sand, silt, clay,colloid, suspension, and specific gravity. The symbols red cross, black dia-mond, blue cross, and gree circle represent IRMI, IMPSEQ, AMELIA, andPCA imputation methods.the observed values and the KNN achieved the lowest PBIAS.For colloid, the RMSE and MAE indicated that median and mean meth-ods gave the highest error, respectively. Similar to the previous variables,KNN outperformed all the imputation techniques by achieving the lowest er-ror. The PBIAS indicated that KNN slightly overestimated the observed val-ues, while PCA underestimated the observed values more than mean method.The low error measurement of KNN and IMPSEQ methods in suspensionand specific gravity, respectively, indicated that these methods are very effi-cient in estimating the missing values in corrosion dataset. The PBIAS forsuspension indicated that KNN has the least bias compared to other tech-niques, and it showed no bias when imputing the missing value for specific694.4. RESULT AND DISCUSSIONTable 4.2: Performance of the imputation techniques on the test dataset.Parameters ErrormetricMeanMedianLR KNNIRMIAMELIAIMPSEQPCASand R2 0.00 0.00 0.77 0.97 0.90 0.94 0.95 0.77RMSE 22.3 22.94 10.7 4.07 7.36 5.49 4.93 10.66MAE 18.75 18.47 8.77 1.23 4.10 3.01 2.69 8.77PBIAS 1.18 21.57 1.02 2.26 0.24 -0.29 -0.58 -1.64Mean 4.86 3.86 2.47 1.11 1.96 1.39 1.35 2.69a20-index 0.19 0.11 0.37 0.91 0.71 0.71 0.71 0.33Silt R2 0.00 0.00 0.24 0.96 0.80 0.88 0.91 0.44RMSE 16.97 17.01 14.81 3.27 7.71 5.77 5.10 12.71MAE 13.58 13.47 10.98 0.74 4.30 3.11 2.79 10.08PBIAS 0.77 2.91 1.97 0.96 1.43 0.19 0.58 -0.09Mean 1.20 1.17 1.15 1.00 1.04 1.02 1.01 1.10a20-index 0.32 0.32 0.49 0.98 0.77 0.86 0.88 0.46Clay R2 0.00 0.00 0.81 1.00 0.94 0.98 0.98 0.87RMSE 20.23 20.22 8.88 0.61 5.40 3.15 2.72 7.45MAE 15.90 15.88 7.51 0.09 2.91 1.58 1.40 6.01PBIAS -3.05 -2.60 -1.22 -0.05 -1.86 -0.04 -0.28 -0.93Mean 1.63 1.62 1.09 1.00 1.11 1.00 1.01 1.10a20-index 0.29 0.26 0.51 1.00 0.81 0.90 0.92 0.55Colloid R2 0.00 0.00 0.95 1.00 0.95 0.97 0.98 0.90RMSE 17.98 18.04 4.21 0.07 4.09 2.90 2.54 6.05MAE 14.16 14.02 3.12 0.00 2.81 2.10 1.91 4.70PBIAS -1.13 5.28 0.16 0.01 -0.79 -0.05 -0.11 -1.94Mean 2.10 1.97 1.07 1.00 1.08 1.03 1.03 1.18a20-index 0.26 0.28 0.70 1.00 0.76 0.83 0.84 0.61Suspension R2 0.00 0.00 0.37 1.00 0.52 0.60 0.65 0.50RMSE 10.52 10.64 8.35 0.67 7.64 6.76 6.27 7.41MAE 8.25 8.24 6.70 0.07 6.15 5.45 5.04 5.99PBIAS 2.06 7.07 0.66 -0.02 -0.55 0.34 0.25 -0.66Mean 1.29 1.22 1.16 1.00 1.08 1.08 1.07 1.12a20-index 0.40 0.42 0.38 0.99 0.45 0.51 0.53 0.45Specific Gravity R2 0.00 0.00 0.61 1.00 0.69 0.71 0.76 0.52RMSE 190.30 190.75 119.04 0.00 108.34 103.32 94.47 131.97MAE 151.11 150.55 93.28 0.00 86.38 79.75 72.69 101.83PBIAS 0.16 0.77 0.44 0.00 -0.26 0.01 -0.09 0.15Mean 1.01 1.00 1.00 1.00 1.01 1.00 1.00 1.00a20-index 0.94 0.94 1.00 1.00 0.99 1.00 1.00 0.97gravity.The µ and a20-index statistical performance indicators were also calcu-lated to indicate the estimation capabilities of the imputation techniques (Alz-abeebee, 2020; Alzabeebee and Chapman, 2020). Mean is calculated as aratio of the predicted to the observed value as shown in Equation 4.16. Theoptimum mean point should be equal to 1. The a20-index evaluates thepercentage of the predicted value that falls within the range of 80%-120%704.4. RESULT AND DISCUSSIONTable 4.3: Statistics of the data used in the NN model.Corrosion Variables Training data Testing dataMin. Max. Mean Std dev. Min. Max. Mean Std dev.Pit depth 0.23 4.09 1.21 0.62 0.25 3.89 1.21 0.64Exposure Time 1.00 17.70 7.30 4.57 1.00 17.70 7.34 4.29Drainage 2.00 4.00 3.00 0.86 2.00 4.00 2.99 0.86Diameter 1.50 3.00 2.27 0.75 1.50 3.00 2.21 0.75Depth 0.56 1.68 0.93 0.22 0.56 1.68 0.93 0.21Sand 1.20 72.00 25.29 22.20 1.20 72.00 23.60 22.34Silt 10.8 78.40 38.91 16.67 10.80 78.40 41.04 17.89Clay 5.60 87.80 35.78 20.15 5.60 87.80 35.33 19.34Colloid 2.70 71.50 27.88 17.53 2.70 71.50 27.54 16.65Suspension 4.40 45.50 23.34 10.75 4.40 45.50 23.70 10.66Resistivity 3.50 451.00 65.88 96.24 3.50 451.00 61.85 86.71Temperature 3.90 20.70 13.03 4.35 3.90 20.70 13.09 4.27Precipitation 386.00 1565.00 940.69 295.56 386.00 1565.00 955.00 300.25Moisture 12.20 51.40 27.35 9.10 12.20 51.40 27.42 8.66Air-pore space 1.90 40.6 11.11 9.24 1.90 40.60 10.94 8.81Specific Gravity 1260.00 2080.00 1735.37 194.29 1260.00 2080.00 1722.33 185.86Volume Shrinkage 0.00 39.80 9.73 11.04 0.00 39.80 8.81 10.18pH 4.40 7.60 6.09 1.13 4.40 7.60 5.99 1.14of the measured value. Like the µ, the optimum value of a-20 index shouldbe closer to 1. Based on both the metrics, KNN, IMPSEQ and AMELIAwere seen to have values closer to 1 in all the variables. From the overallevaluation of the error metrics, KNN and IMPSEQ were found to have abetter imputation capability in corrosion dataset.Following the comparison of the imputation techniques, the utility of thetechniques were evaluated using a NN model. After applying deletion tech-nique to the original corrosion dataset (2160 records), the complete dataset(1432 records) was divided into training (1002 records) and testing (430records) datasets, in which the training dataset was used to train the NNmodel and the test dataset was used to measure the generalization perfor-mance of the model. Table 4.3 shows the maximum (max.), minimum (min.),mean (mean) and standard deviation (std dev.) values for the training andtesting sets used in the model development (Alzabeebee, 2019). The NNmodel was developed in Python using Keras. During model development,the categorical attributes (internal drainage and diameter) were handled us-ing One-Hot Encoding and the numerical attributes were normalized to speedup the model learning and lead to faster convergence. Activation functionsof ReLU and linear were used in the hidden and output layers, respectively.The best network architecture was found to have 3 hidden layers with 20neurons in each layer. The NN model used Adam optimizer to calculate theoptimum network weights that minimize the loss function. It took the model714.4. RESULT AND DISCUSSION0 0.5 1 1.5 2Observed Pit depth (mm)0123Predicted Pit depth (mm) R2 = 0.81Figure 4.8: Comparison of the observed and predicted values of the testingset (Baseline Model). The red line indicates line of best fit.7.42 s to run on Windows 10 with 2.60 GHz dual processors and 128 GB ofRAM.The trained NN model captured the underlying relations between thecorrosion factors and pit depth. The training and testing accuracy of theNN were found to be 82% and 81%, respectively. The high testing accuracyindicated that the model was able to generalize on the dataset that has notbeen used for training. A comparison of the observed and model predictedpit depth values is shown in Fig. 4.8. The points are fairly distributed alongthe line of equality. This accuracy (81%) is taken as a baseline accuracybecause it’s trained on the complete values of the original corrosion datasetwithout estimating the missing values. Similarly, the model was trained onthe imputed datasets and the result is shown in Table 4.4.From Table 4.4, it can be observed that the training and testing accuracyof the model is very close on all the imputed datasets. This indicates thatthere was no overfitting problem and the model was able to generalize onthe testing dataset that has not been used during training. Furthermore, thedatasets imputed by Mean (76%), Median (74%) and LR (77%) resulted in arelatively low model accuracy, whereas the datasets imputed by KNN (85%),IRMI (80%), AMELIA (84%), IMPSEQ (91%) and PCA (81%) resulted in ahigher model accuracy. Deletion technique outperformed Mean, Median andLR. It can be concluded from here that single imputation methods does notperform good when the original dataset has high number of missing values.724.4. RESULT AND DISCUSSIONTable 4.4: Model performance on the datasets created by the different im-putation techniques.Models Training TestingDeletion 0.82 0.81Mean imputation 0.77 0.76Median imputation 0.76 0.74LR imputation 0.79 0.77KNN imputation 0.86 0.85IRMI imputation 0.83 0.80AMELIA imputation 0.85 0.84IMPSEQ imputation 0.95 0.91PCA imputation 0.82 0.81Comparison of the observed and model predicted pit depth values for the twobest techniques (IMPSEQ and KNN) is given in Fig. 4.9. The model accu-racy on the dataset imputed by KNN (85%) and IMPSEQ (91%) surpassedthe baseline accuracy (81%) and this can confirm that both techniques areuseful to estimate the missing value in corrosion dataset. Since IMPSEQleads to a higher R2, it is taken as a best technique among the remainingimputation techniques.0 1 2 3 4 5 6Observed Pit depth (mm)0123456Predicted Pit depth (mm)R2 = 0.85(a) Model trained by the dataset im-puted by KNN0 1 2 3 4 5 6Observed Pit depth (mm)0123456Predicted Pit depth (mm)R2 = 0.91(b) Model trained by the dataset im-puted by IMPSEQFigure 4.9: Comparison of the observed and predicted pit depth of the testingset. The red line indicates line of best fit.Finally, a sensitivity analysis was performed using all the datasets to as-734.5. SUMMARY-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%Exposure Time (years)Internal DrainageDiameter (in)Depth (m)Sand (%)Silt (%)Clay (%)Colloid (%)Suspension (%)Resistivity (ohm-m)Temperature (Celsius)Precipitation (mm)Moisture EquivalentAir-pore spaceSpecific GravityVolume ShrinkagepHPCAIMPSEQAMELIAIRMIKNNLRMedianMeanDeletionFigure 4.10: Relative contribution of the corrosion factors.sess the relative contribution of the corrosion factors in pit depth prediction.Spearman rank correlation and tornado graph were generated as describedby Tesfamariam et al. (2006). The Tornado graph is shown in Fig. 4.10. Allthe datasets captured that exposure time has the highest positive contribu-tion and this can be related with the reports of previous study (Romanoff,1957; Ricker, 2010; Melchers, 2004). The remaining variables have varyingdegree of contribution depending on the sign of the correlation.4.5 SummaryCorrosion prediction models have a key role in the implementation ofpreventive strategies. The prediction accuracy of a corrosion models canbe enhanced by solving the challenges of data scarcity and missing values.A dataset with high missing rate leads to bias in decision making. In thispaper, eight imputation techniques are applied to handle the missing valuesin a corrosion database and assist pipe asset owners in decision making byenhancing the prediction accuracy of the corrosion models.The imputation techniques are mean, median, LR, KNN, IRMI, AMELIA,744.5. SUMMARYIMPSEQ and PCA based imputation. These methods are applied on a testdataset, which has a missing rate of 30%. In general, for a dataset withmoderate to high missing rate, mean and median imputation methods givesunsatisfactory result due to assigning a single value for all the missing values.This basically underestimates the variance of the data. The performance ofLR model also decreases if the explanatory variables have missing values. Forinstance, the NBS data has a lot of variables with concurrent missing val-ues. The performance of both IRMI and AMELIA were lower than IMPSEQ.Overall KNN and IMPSEQ performed better in estimating the missing valuesin NBS corrosion dataset. Utility of estimating missing values in a corrosiondataset was shown by comparing the performance of a NN model trainedon datasets handled by the imputation techniques. The datasets created bymultiple imputation methods outperformed those created by single imputa-tion methods, and specifically datasets created by KNN and IMPSEQ foundto lead to a higher model accuracy. The R2 performance increased from thebaseline accuracy (81%) when the model was trained by KNN (85%) andIMPSEQ (91%) imputed datasets.The NBS data may not be applicable for predicting corrosion pit depthsin the steel of the currently in use oil and gas pipelines because it is outof date. Nonetheless, the proposed methodology can be applied to solve amissing values problem in any corrosion dataset. This study can be extendedin the future by applying the imputation techniques to a corrosion datasetcollected from scattered sites. This can be useful to understand how muchthe techniques are robust to a spatially sparse data. Further research onKNN imputation may take into account how the function performs withdifferent K values rather than just utilizing a fixed default value. Moreover,other imputation techniques like Bayesian can be considered to account thestochastic character of corrosion and factors affecting the process.75Chapter 5Handling class imbalance in acorrosion pit measurementdatabase5.1 BackgroundMachine learning (ML) based algorithms, due to their ability to modelnonlinear and complex relationship, have been used in predicting corrosionpit depth in oil and gas pipelines. Class imbalance and data scarcity arethe challenging problems while training ML models. This paper utilized aconditional generative adversarial network (cGAN) to handle class imbalanceproblem in a corrosion dataset by generating new samples. Utility of thecGAN data augmentation is evaluated by training an artificial neural network(ANN) model. In addition, random oversampling and Borderline-SMOTEdata generating techniques are used for comparison with cGAN. The testingaccuracy of the ANN model increased greatly when trained by the cGANbased augmented dataset and this model performance improvement can beuseful for a pipeline integrity management.5.2 Data collectionThe corrosion data was obtained from the study conducted by Velázquezet al. (2009). The database was collected from more than 250 excavationsites in southern Mexico over a three-year period. Any metal loss causedby corrosion that has a diameter equal to or less than two times the pipewall thickness was taken as pitting. The corrosion database consisted of 259samples, and 9 out of these are outliers. Several methods, including a box-plot, standardized residuals, Cook’s distance, and the hat matrix, are usedin the original study to identify the outliers. The presence of outliers reducesmodel accuracy, hence they are often removed from research to prevent bias.The pipes had been buried in various soil types, which were categorized into765.2. DATA COLLECTIONFigure 5.1: Number of observations for the different soil types.textural classes used by the U.S. Department of Agriculture (Fig. 5.1) as clay(C), sandy clay loam (SCL), clay loam (CL), silty clay loam (SY CL), siltyclay (SC) and silt loam (SL). The soil types have an imbalanced sample ofobservation: Clay (107) , sandy clay loam (75), clay loam (59), silty clay loam(6), silty clay (2) and silt loam (1). Velázquez et al. (2009) have consideredall observed samples (All), clay, sandy clay loam and clay loam categories intheir analysis because they have a large enough number of samples to ensurestatistical significance.The details of the database published in a separate technical note byVelázquez et al. (2010) included maximum pit depth, soil and pipe relatedfactors. The factors that represent soil properties are redox potential (rp),pH (ph), pipe-to-soil potential (pp), soil resistivity (re), soil textural class(Class), water content (wc), soil bulk density (bd), dissolved chloride (cc), bi-carbonate (bc), and sulfate (sc) ion concentrations. The pipe related factorsare age of pipeline (t) and coating type (ct). The symbol of the variables istaken from Velázquez et al. (2010) corrosion database. A pairwise correlationof these variables is shown in Fig. 5.2, where a value of +1 for the Pearson’scorrelation indicates total positive correlation, and -1 indicates total negativecorrelation. The highest correlation is observed between bicarbonate contentand chloride content (0.61). There was no significant correlation observedbetween the remaining variables. Hence, all the variables are used in theanalysis. Furthermore, pairwise scatter plot between the variables is shownin Fig. 5.3 for the different soil types. There is a class overlap in almost allthe variables, except a distinct distribution is observed when compared usinga bulk density. In the current study, silty clay loam, silty clay and silt loam775.3. METHODOLOGYFigure 5.2: Pairwise correlation of the corrosion variables.are aggregated and categorized under mixed soil class in order to enhancetheir visibility. Throughout the remaining paper, clay soil type is taken asa majority class and remaining soil types (sandy clay loam, clay loam andmixed soil) as minority classes.5.3 MethodologyThe objective of this study is to solve the class imbalance problem in thecorrosion database. Majority of the maximum pit depth measurements in thecorrosion database are for pipes buried in clay soil type, and there are lessermeasurements for the remaining soil types. As a result, class imbalance han-dling techniques are used to generate new samples related to the soil and pipefactors in the minority classes. The techniques developed includes randomoversampling, borderline SMOTE, and cGAN. A flowchart of the proposedstudy is shown in Fig. 5.4 using an example of the cGAN technique. The785.3. METHODOLOGYFigure 5.3: Pairwise scatter plot of the corrosion variables.methodology includes data generating using the techniques, and evaluatingthe utility of these technique using artificial neural network algorithm. Priorto any model development, the corrosion database was pre-processed anddivided into training and testing dataset, each containing 80% and 20% ofthe corrosion database, respectively. The pre-processing steps includes re-moving outliers from the corrosion database and data normalization. Theoutliers are already identified in Velázquez et al. (2009). For data normal-ization, MinMaxScaler in Python is used to transform the numerical inputdata into a standard range. Following, the training dataset was used to trainthe class imbalance handling techniques and generate new samples. The soiltype for the minority classes is used as an input to the cGAN model. Thegenerated samples are soil parameters, which includes redox potential, pH,pipe-to-soil potential, soil resistivity, water content, soil bulk density, chloridecontent, bicarbonate content, and sulfate content. Pipeline can have differ-ent exposure time and coating type, and thus samples corresponding to these795.3. METHODOLOGYTraining DatasetData pre-processing (Cleaning and normalizationTesting DatasetcGAN Model Generated DatasetNeural network Model result (RMSE and R2)Corrosion datasetAugmented Dataset = Training + GeneratedFigure 5.4: Work flow of the proposed study.variables are generated randomly. The coating types from which the sam-ples are generated includes non-coated (NC), asphalt-enamel-coated (AEC),wrap-tape-coated (WTC), coal-tar-coated (CTC), and fusion-bonded-epoxy-coated (FBE). For the exposure time, random integers between 5 and 50years are generated and taken as new samples for the variable. The maxi-mum pit depth that corresponds to the generated soil and pipe factors arepredicted using the equations recommended in Velázquez et al. (2009). Fi-nally, the generated and training datasets are combined together to create anew dataset, named in this study as augmented dataset. The importance ofgenerating new dataset is assessed by comparing the prediction performanceof a neural network that was trained both on the training dataset and aug-mented dataset. After training with each dataset, the model was validatedusing the testing dataset. Details of borderline SMOTE, cGAN and ANNmodels are given in the subsequent subsections.5.3.1 Borderline-SMOTESMOTE is one of the oversampling techniques used to address a classimbalance problem. In a dataset with high imbalance and class overlap,algorithms should attempt to learn the borderline of each class during train-ing in order to achieve a better prediction. Borderline-SMOTE is based onSMOTE technique to oversample or strengthen the borderline minority ex-805.3. METHODOLOGYRealFakeDݔ ̱ ܲ݀ ܽݐܽሺݔሻ ݔ ̱ ܲ݃ ሺݔሻGݖ ̱ ܲݖሺݖሻFakeReal(a)RealFakeDݔ ̱ ܲ݀ ܽݐܽሺݔȁݕሻ ݔ ̱ ܲ݃ ሺݔȁݕሻGݖ ̱ ܲݖሺݖሻFakeRealݕ ̱ ܲ݀ ܽݐܽሺݕሻ(b)Figure 5.5: Structure of (a) GAN and (b) cGAN.amples (Han et al., 2005). Interested readers are referred to Han et al. (2005)for handling imbalanced data using Borderline-SMOTE.5.3.2 Generative Adversarial Network (GAN)Generative Adversarial Network (GAN) is a framework designed to trainimplicit generative models using neural networks (Goodfellow et al., 2014).The model approximates the underlying distribution of real data in order togenerate samples that have similar distribution with the real data. Generator(G) and discriminator (D) are the two networks of GAN as shown in Fig.5.5 a. The objective of the G is to produce synthetic data to fool the D. Theobjective of the D is to distinguish between the samples coming from theoriginal dataset and the samples coming from the G. Both the G and the Dact as adversaries with respect to each other to produce real like samples.The aim of the G is to capture the data distribution. This can be rep-resented as G : Z → X; where Z and X corresponds to a noise space ofarbitrary dimension dz that corresponds to a hyperprameter and data space,respectively. The task of the D is to estimate the probability that a givensample is real, and this can be represented as D : X → [0, 1]. The trainingof GAN involves the D and G to play the following minimax game withobjective function V (G,D) as (Goodfellow et al., 2014):815.3. METHODOLOGYminGmaxDV (D,G) = Ex∼Pdata(x) [log(D (x) )]+Ez∼Pz (z) [log (1−D (G(z)))](5.1)The x ∈ X and z ∈ Z values are sampled from the data distributionPdata(x) and noise distribution Pz(z), respectively. The Pz can be eitheruniform or standard Gaussian distribution. The training procedure consistsof alternating between k steps of optimizing for D and one step of optimiz-ing for G. During training, the D model is optimized to correctly classifysamples coming from the G and original dataset by assigning 0 and 1, respec-tively. Contrary, the G model is optimized to confuse the discriminator bygenerating real like samples and assigning the label 1 to these samples. Theunique solution of this adversarial game corresponds to pg = Pdata, where pgindicates distribution of the G, and D(x) = 0.5 for any input (Goodfellowet al., 2014; Douzas and Bacao, 2018). The optimization of the GAN stopswhen the D is no longer able to distinguish the generated data from the realdata.The cGAN model is an enhancement of GAN proposed by Mirza andOsindero (2014). A typical schematic of cGAN is shown in Fig. 5.5 b. IncGAN, both the G and D receives an additional information Y (e.g., soiltype is used in this study) to control the data generation process in a super-vised manner. With the class information, cGAN can control the numberof instances corresponding to a particular label which is impossible for stan-dard GAN (Hong et al., 2019). The cGAN framework modifies the G and Dnetworks to include the additional information as (Douzas and Bacao, 2018):G : Z × Y → X (5.2)D : X × Y → [0, 1] (5.3)Similarly, the objective function in Equation 5.1 is modified to reflect theredefinition of the G and D as (Mirza and Osindero, 2014):minGmaxDV (D,G) = Ex,y∼Pdata(x,y) [log(D (x, y) )]+Ez∼Pz (z),y∼P (y) [log (1−D (G(z, y), y))](5.4)The (x, y) ∈ X × Y values are sampled from the data distributionPdata(x, y). The z ∈ Z and y ∈ Y values are sampled from the noise825.3. METHODOLOGYdistribution Pz(z) and conditional data vectors in the training data thatis represented by the density function Pdata(y), respectively. The cGANframework has a similar training procedure as the GAN framework. Forclarity, equation 5.4 is rephrased in terms of cost functions. The cost equa-tions for the gradient update of the D and G on a minibatch of m trainingexamples {(xi, yi)}mi=1 and m noise samples {(zi)}mi=1 are simple logistic costexpressions, written as (Douzas and Bacao, 2018):JD = − 12m(m∑i=1logD(xi, yi) +m∑i=1log(1−D(G(zi, yi), yi)))(5.5)JG = − 1mm∑i=1logD(G(zi, yi), yi) (5.6)Like the GAN framework, the cGAN model is trained by alternatingbetween k steps of optimizing forD (equation 5.5) and one step of optimizingfor G (equation 5.6).5.3.3 Artificial Neural NetworkArtificial neural network is a ML algorithm which is composed of aninput layer, hidden layer, and output layer. The layers are connected toeach other by weighted connections, and the weights are calculated throughthe training process according to the chosen algorithm (Sousa et al., 2014).Backpropagation is a frequently used training methodology that adjusts theweights by minimizing the error between the model output and the observedvalues (Hassoun, 1995). The general equation for an ANN is written asfollows:y = f(∑i=1wi xi + b)(5.7)where y is output of the model, f(·) is activation function, wi is weightcorresponding to input xi, and b is bias. A loss function is used to measurethe difference between the model output and observed values. A minimumerror indicates that the model is able to approximate mapping relationshipbetween the input and output layers (Hassoun, 1995). The deep architec-ture neural network has better generalization ability compared with shallowarchitecture (e.g., neural network with less than three hidden layers) whenmodeling a complex system (Bengio, 2009). The architecture of the NNis shown in Fig. 5.6, where the inputs and outputs in the current studyrepresents the corrosion factors and pit depth, respectively.835.4. RESULTS AND DISCUSSIONPit depthCorrosion factorsFigure 5.6: Structure of the neural network.5.3.4 Performance evaluationThe performance of the NN model was assessed using coefficient of deter-mination (R2) and root mean square error (RMSE). A higher R2 and smallerRMSE value indicate a good prediction capability of the model. Mathemati-cally the performance indicators are expressed using the following equations:R2 = 1−∑Ni=1(Yi − Yˆi)2∑Ni=1(Yi − Y¯i)2(5.8)RMSE =√∑Ni=1(Yi − Yˆi)2N(5.9)where Yi is observed value, Yˆi is predicted value, Y¯i is mean of the obser-vations, and N is number of data points in the testing dataset.5.4 Results and discussionAfter removing outliers in the original corrosion dataset (259 records),the complete dataset (250 records) was divided into training (200 records)and testing (50 records) datasets. The soil type in the training datasetconstitutes: Clay (86), sandy clay loam (59), clay loam (48), mixed soil (7).The soil type in the testing dataset constitutes: Clay (21), sandy clay loam(16), clay loam (11), mixed soil (2). Table 5.1 shows the maximum (max.),minimum (min.), mean (mean) and standard deviation (std dev.) values forthe training and testing sets used in the model development.845.4. RESULTS AND DISCUSSIONTable 5.1: Statistics of the data used in the NN model.Corrosion Variables Training data Testing dataMin. Max. Mean Std dev. Min. Max. Mean Std dev.Pit depth (mm) 0.41 10.41 1.89 1.80 0.43 8.41 2.02 1.86Exposure time (years) 6.00 50.00 23.12 9.05 5.00 42.00 22.58 8.70pH 4.14 9.88 6.14 0.93 4.35 8.14 5.99 0.85Pipe-to-soil potential (V ) -1.97 -0.42 -0.88 0.24 -1.36 -0.47 -0.85 0.19Soil resistivity (Ω.m) 1.90 399.50 48.94 53.24 3.80 276.70 53.30 57.41Water content (%) 12.80 57.60 23.80 6.12 8.80 38.90 23.27 6.12Soil bulk density (g/mL) 1.11 1.49 1.30 0.08 1.11 1.55 1.30 0.09Chloride content (ppm) 0.99 351.00 43.39 60.08 6.62 292.5 35.97 48.97Bicarbonate content (ppm) 0.99 156.20 18.97 22.27 3.91 91.12 15.40 13.63Sulfate content (ppm) 0.99 1370.20 152.92 168.00 0.99 590.40 131.81 101.87Redox potential (mV ) 2.10 342.00 163.26 85.46 23.00 348.00 188.92 86.865.4.1 Results for data augmentation using ROS, SMOTEand cGANThe random oversampling and Borderline-SMOTE models are imple-mented in Python using the RandomOverSampler and BorderlineSMOTEpackages for handling imbalanced dataset. The cGAN model is implementedin Python using the code developed by Aggarwal et al. (2019). Consideringthe sensitivity of training a deep network, a pre-processed training datasetwas used to train the cGAN model. The soil types label and noise vectorsampled from a Gaussian distribution are feed to the generator to train thecGAN model (Fig. 5.5 b). Implementation of a cGAN model requires anextensive fine-tuning process to handle the parameters, such as the numberof hidden layers, hidden neurons, number of epochs, and mini-batch sam-ple size. A trial and error method was used to select the best architectureof generator and discriminator. The fine-tuned network architecture of thegenerator and discriminator are summarized in Table 5.2. The discriminatorfeeds the inputs of soil types label and generator’s output of soil parame-ters, and distinguishes between the real and synthetic data using a sigmoidactivation. Adam and Adamax optimizers are found to be relatively stableand achieve a good model output Aggarwal et al. (2019). The parametersβ1 = 0.9 and β2 = 0.99 remained with their default values, and learningrates 0.0015 and 0.00048 are used for the discriminator and generator, re-spectively. The training steps to update the networks parameter depend onthe batch size. The number of training samples used to estimate the errorgradient is referred to as the batch size. A batch size of 16, for instance,means that the error gradient will be estimated using 16 samples from thetraining dataset before the model weights are updated. In this study, a855.4. RESULTS AND DISCUSSIONTable 5.2: Architecture of both generator and discriminator.Network Layers Generator DiscriminatorNeurons Activation function Neurons Activation functionInput Layer (2,16) ReLU (2,16) ReLUHidden Layer (16,16) ReLU (16,32) ReLUHidden Layer (16,32) ReLU (32,64) ReLUHidden Layer (32,64) ReLU (64,64) ReLUHidden Layer (64,64) ReLU (64,128) ReLUOutput Layer (64,9) Linear (128,1) Sigmoidbatch size of 16 is found to have a desirable output. Although the output ofa cGAN is remarkable, it is challenging to obtain a stable model.The random oversampling, Borderline-SMOTE, and cGAN techniquesgenerated synthetic data for all the minority soil types to have more numberof samples. The generated data constitutes: sandy clay loam (27), clay loam(38), mixed soil (79). Hence, each of the soil types will have equal number ofsamples (86). Subsequently, the original data is combined with the generateddata to make up the augmented dataset (344). The distribution of the gen-erated and original data for the random oversamplilng, Borderline-SMOTE,and cGAN are compared as shown in Figs. 5.7, 5.8 and 5.9. The generatedand original data are indicated by Blue and Red colors, respectively. Over-all, the data generated by the three techniques have similar distribution withthe original data, and cGAN specifically proved that it can give a reasonabledistribution from a small dataset.5.4.2 Result for pit depth prediction using ANNThe usefulness of the generated data are evaluated using an ANN model.The ANN was developed using the Keras model on Python. During modeldevelopment, the categorical attributes (coating type and soil type) werehandled using One-Hot Encoding and the numerical attributes were normal-ized to speed up the model learning and lead to faster convergence. Activa-tion functions of LeakyReLU and linear were used in the hidden and outputlayers, respectively. The network architecture has 2 hidden layers with 64neurons each. In addition, a dropout of 0.2 or 0.3 is used for each datasetto avoid an overfitting problem. The ANN model took in average about 150s each to run the augmented datasets on Windows 10 with 2.80 GHz dualprocessors and 16 GB of RAM.The performance of the ANN model is given in Table 5.3, where the resultis given for the original and augmented datasets. The ANN model captured865.4. RESULTS AND DISCUSSION   S+)UHTXHQF\   SS  UH ZF)UHTXHQF\ EG   FF   EF)UHTXHQF\ VF   USFigure 5.7: Histogram comparison between original and augmented data byrandom oversampling (red color indicates the original data and blue colorindicates the augmented data).the underlying relations between the corrosion factors and pit depth Seghieret al. (2021). The training and testing accuracy (RMSE) of the originaldataset are 86% (0.75) and 81% (0.71), respectively. A higher training ac-curacy is reported in Seghier et al. (2021), but their model training andtesting accuracy has a big difference. This can be due to an overfittingproblem. However, in the current study, the similarity between the trainingand testing accuracy indicates that the ANN model can be able to general-ize with a good accuracy in unseen corrosion dataset. A comparison of theobserved and model predicted pit depth values is shown in Fig. 5.10. The875.4. RESULTS AND DISCUSSION  S+)UHTXHQF\   SS  UH ZF)UHTXHQF\ EG   FF  EF)UHTXHQF\ VF   USFigure 5.8: Histogram comparison between original and augmented databy Borderline-SMOTE (red color indicates the original data and blue colorindicates the augmented data).solid diagonal line in the plot indicates line of perfect prediction, in whichthe predicted equals the observed corrosion pitting depth. The points arefairly distributed along the line of equality in the training phase. The test-ing accuracy (81%) is taken as a baseline accuracy for comparison with themodel performance when trained using the datasets augmented by randomoversampling, Border-SMOTE, and cGAN techniques.The training and testing accuracy (RMSE) of the random oversamplingaugmented dataset are 99% (0.34) and 80% (0.73), respectively. The trainingand testing accuracy (RMSE) of the Borderline-SMOTE augmented dataset885.4. RESULTS AND DISCUSSION   S+)UHTXHQF\  SS  UH ZF)UHTXHQF\  EG   FF  EF)UHTXHQF\ VF   USFigure 5.9: Histogram comparison between original and augmented data bycGAN (red color indicates the original data and blue color indicates theaugmented data).are 99% (0.45) and 84% (0.77), respectively. The training and testing ac-curacy (RMSE) of the cGAN augmented dataset are 96% (0.62) and 90%(0.65), respectively. In general, the augmented datasets improved the train-ing accuracy of the model compared to the original dataset. The testingaccuracy varies for each dataset, in which cGAN gave the lowest RMSE andhighest coefficient of determination, followed by Borderline-SMOTE, andrandom oversampling. The closeness between training and testing accuracyof cGAN indicates that there is no overfitting problem with the data. The895.4. RESULTS AND DISCUSSIONTable 5.3: ANN model performance on the training and testing dataset.Models Training data Testing dataR2 (%) RMSE R2 (%) RMSEANN (Original dataset) 86 0.75 81 0.71ANN (Random oversampling) 99 0.34 80 0.73ANN (Borderline-SMOTE) 99 0.45 84 0.77ANN (cGAN) 96 0.62 90 0.65     2EVHUYHGSLWGHSWK PP 3UHGLFWHGSLWGHSWK PP 5ð (a)     2EVHUYHGSLWGHSWK PP 3UHGLFWHGSLWGHSWK PP 5ð (b)Figure 5.10: Comparison of observed and predicted corrosion pit depth.The figures are obtained from the ANN trained with the original dataset:(a) Training accuracy and (b) Testing accuracy.testing accuracy increased from baseline accuracy 81% to 90% due to themodel improvement in predicting the corrosion pit depth observed in pipesthat are buried in the minority soil samples, and this indicates that the gen-erated samples improve ability of the ANN to generalize in unseen testingdataset. The smaller RMSE in both training and testing dataset also an-other indicator of the model improvement. A comparison of the observedand model predicted pit depth values for the cGAN based dataset is shown inFig. 5.11. The model slightly underestimated and overestimated the largerand smaller corrosion pit values in the training dataset, respectively. How-ever, majority of the points are fairly distributed along the line of equalityin the testing phase.Corrosion prediction models have been used in reliability analysis of oil905.5. SUMMARY     2EVHUYHGSLWGHSWK PP 3UHGLFWHGSLWGHSWK PP 5ð  (a)     2EVHUYHGSLWGHSWK PP 3UHGLFWHGSLWGHSWK PP 5ð  (b)Figure 5.11: Comparison of observed and predicted corrosion pit depth. Thefigures are obtained from the ANN trained with the cGAN based augmenteddataset: (a) Training accuracy and (b) Testing accuracy.and gas pipelines to predict the maximum corrosion defect observed in apipeline segment. The corrosion model is then integrated with other models,such failure pressure and reliability model, to predict the failure probabilityof a pipeline segment due to maximum corrosion defect (Chalgham et al.,2020; Shabarchin and Tesfamariam, 2016). Hence, enhancement of the cor-rosion model can help improving failure probability prediction of oil and gaspipelines that is important in a pipeline integrity management. In the fu-ture work, the maximum pit depth that corresponds to the generated soiland pipe factors can be predicted using advanced ML algorithms instead ofusing empirical equations. This can further improve the output result.5.5 SummaryPipeline failure can have adverse consequences related to public health,safety and heavy financial liabilities. Utilities need a proactive managementpractices to increase their asset maintenance system. ML based models havebeen used to model the complex and nonlinear deterioration process of oiland gas pipes. However, performance of the ML is affected when trainedwith imbalanced and small dataset. This paper proposed different data aug-menting methods to generate new soil parameters based on different soil915.5. SUMMARYtypes. The generated data have similar distribution with the original data,hence the techniques are assumed useful in building a balanced corrosiondatabase for asset management. Utility of this concept is evaluated usingan ANN model on the original and augmented datasets, where the model istrained using both datasets and tested using a testing dataset that has notbeen used for training in any part. The testing accuracy of the ANN modelincreased greatly when trained by the cGAN based augmented dataset andthis model performance improvement can be useful for the pipeline integritymanagement.The current work can be expanded in the future by comparing cGANto other GAN variants and architectures that minimize the instability inGAN models (Engelmann and Lessmann, 2021). Furthermore, a risk-baseddecision making frame work can be developed by integrating the corrosiondefect prediction model with a reliability and consequence models. The pipesclassified as high-risk pipes will be prioritized for maintenance, rehabilitation,and replacement or inspection. Finally, the concept of the proposed cGANbased data augmentation can be further extended to the other utilities (e.g.,roads and water pipes).92Chapter 6Failure assessment of oil andgas pipelines using anintegrated BBN-GIS model6.1 BackgroundCorrosion is one of the main reasons for pipeline failure in the oil andgas industry. Because a pipeline failure can result in serious personal in-jury, monetary loss, and environmental damage, pipeline operators need tomake timely, and cost-effective decisions to prevent accidents in high con-sequence areas. Most of the proposed models are developed and computedat the component level (a pipe segment), and there are just a few studiesthat focus on the system level, which spans hundreds of miles under variousenvironmental conditions. The current study proposed integrating GIS andBayesian network to estimate the likelihood that transmission pipelines mayfail due to localized external corrosion. To calculate the failure probabilityof the pipe infrastructure, the model incorporates data from a large net-work of pipes that spans hundreds of miles in length and their surroundings.The spatial GIS capabilities combined with the Bayesian network’s reasoningpower creates a powerful tool for calculating the probability that transmis-sion pipelines will fail in a given area based on the information currentlyavailable.6.2 Spatial Bayesian network6.2.1 Bayesian belief networkA Bayesian belief network (BBN), sometimes referred to as a Belief net-work or a Bayes network, is a graphical model for probabilistic reasoning thatrepresents the joint probability distribution among a number of random vari-ables. The model was initially presented by Pearl (1988), and since then, it936.2. SPATIAL BAYESIAN NETWORKhas been used as a probabilistic modelling tool in a number of research topics(Wu et al., 2017; Landuyt et al., 2013; Kabir et al., 2015). A BBN is madeup of two structural components: a directed acyclic graph (DAG) and a con-ditional probability table (CPT). A DAG is a qualitative component madeup of a causal network that incorporates the important network variables(represented by nodes) and their cause-and-effect relationships (representedby directed edges) (Guo et al., 2020b; Zhang et al., 2019). A CPT is a quan-titative component that quantifies the cause-and-effect connections betweenthe network’s variables. Figure 6.1 shows a schematic of a Bayesian network.The topology of a BBN can be represented by the notation G(X,E), whereX denotes a set of variables and E denotes a set of edges in a graph G,respectively. Each variable has discrete states. A directed edge is a linkfrom a parent node to a child node that reflects the conditional dependentrelationships between variables. For instance, in Figure 6.1, a link from X1and X2 to X3 indicates that the value of X3 is dependent on value of parentnodes X1 and X2. The CPTs specify the degree of belief that a variable willbe in a specific state as determined by a state of the parent variable (Guoet al., 2020b). The absence of an edge connecting two nodes in X (e.g., thereis no connection between X1 and X2) indicates the independence of the twonodes. The strength of a relationship between variables is defined in theCPT of each child node. If a node has no parent node (e.g., X1 and X2),then the CPT changes into unconditional probability (UP).A BBN combines knowledge of graph theory and the Bayesian theorem.The Bayes’ theorem has been proven to be a coherent method to manageuncertainty by explicitly representing the conditional probability dependen-cies of variables (Ismail et al., 2011; Kabir et al., 2015). In this case, theBayesian theorem is mathematically expressed as followsp(X3|X1, X2) = p(X1, X2|X3)× p(X3)p(X1, X2)(6.1)where p(X3|X1, X2) is the posterior probability of X3 based on the dataor evidence X1 and X2, p(X1, X2|X3) is the likelihood function, and p(X3)is the prior probability. The denominator p(X1, X2) represents the totalprobability. The use of probabilities enables a BBN to handle uncertaintyin the input variables and relationships between all the variables. Theseuncertainties propagate throughout the network, and as a result, the modelprediction takes uncertainties into consideration (Pearl, 1988; Guo et al.,2020b). The advantage of employing BBN is that the model can utilizethe knowledge from both the data and the experts (Cheng et al., 2002).When new information is acquired, the probabilistic relationships between946.2. SPATIAL BAYESIAN NETWORKVariable X1Variable X2Variable X3Variable X1 ProbabilityH P(X1= H)M P(X1= M)L P(X1= L)Variable X2 ProbabilityH P(X2= H)M P(X2= M)L P(X2= L)Variable X1 Variable X2Variable X3ProbabilityH M LH H P(X3= H|X1= H, X2= H) P(X3= M|X1= H, X2= H) P(X3= L|X1= H, X2= H)… … … … …L M P(X3= H|X1= L, X2= M) P(X3= M|X1= L, X2= M) P(X3= L|X1= L, X2= M)L L P(X3= H|X1= L, X2= L) P(X3= M|X1= L, X2= L) P(X3= L|X1= L, X2= L)Unconditional probability (UP) Unconditional probability (UP)Conditional probability table (CPT)Figure 6.1: A schematic of BBN (modified after Cockburn and TesfamariamCockburn and Tesfamariam (2012) ).the various nodes can be updated further. The model is also flexible toperform diagnostic (bottom-up analysis) and predictive (top-down inference)analysis. In predictive analysis, the cause (parent nodes) is observed, andpossible effects are inferred (child node). In diagnostic analysis, the effect(child node) is observed, and the possible cause is inferred (parent nodes)(Ismail et al., 2011; Cockburn and Tesfamariam, 2012; Kabir et al., 2015).The discretization of continuous data is a crucial step in implementingBBN model. This process invariably leads to information loss, the severityof which depends on the discretization method and the number of intervals.Nojavan A et al. (2017) discussed about the equal interval, equal quantile,and moment matching discretization techniques. The data is divided intointervals of equal length using equal interval. When the data distribution isroughly uniform, this approach works well. The equal interval method mightnot be ideal when the underlying distribution of the variable is not uniformor when outliers are present in the data. Data is divided into groups withequal sample sizes using equal quantile. This approach is intended to capturedistributions that have one or more modes. The discretized distribution’smoments are matched with the continuous data’s moments in the momentmatching method. Interested readers are referred to Nojavan A et al. (2017)for comparative analysis of the three discretization techniques.956.3. METHODOLOGY6.2.2 Integrating BBN with GISThe spatially explicit technique, which combines BBN and GIS, is utilisedin the decision-making process for failure assessment of oil and gas pipelines.The spatial application of BBN is implemented using a R package calledbnspatial. The packages accepts a Bayesian network developed and saved in.net file format. As a result, the BBN is developed in GeNIe software andsaved in the .net format that can be imported in bnspatial. The input datato the BBN are downloaded as a raster file and analyzed in Quantum GIS(QGIS), which is a free and open-source software to perform GIS application.CPT is used to quantify the relationships between related nodes in the BBN.The package used the input raster grid data and then performed the BBNinference computations for each raster grid cell. Raster maps are producedby the model, and each cell on the map reflects the failure probability forthe target variable as determined via BBN inference (Guo et al., 2020b).6.3 MethodologyThe current study introduces a spatial BBN technique as a tool for es-timating the failure probability of oil and gas pipelines exposed to externalpitting corrosion. Figure 6.2 depicts the proposed study’s flowchart, whichhas six steps. Downloading spatial input datasets, cleaning the data, and ras-terizing the vector data (converting vector to raster) make up the first threeprocesses. QGIS is used to carry out these tasks. The pipeline network in-formation is downloaded from AER webiste (https://www.aer.ca/providing-information/data-and-reports/maps-mapviewers-and-shapefiles). It containscertain details on pipes, such as their outside diameter, wall thickness, typeof coating, and maximum operating pressure (MAOP). Then, using certaincriteria or expressions, such as diameter (equal to or greater than 323.9mm),maximum operating pressure (equal to or greater than 3475KPa), buriedpipelines, and operation status, transmission pipelines are extracted fromthe network for further analysis. The age of pipe segments is calculated us-ing the permit expiry date information, which is the date that a pipe segmentor installation will change from "Permitted" status to "Operating" status,typically one year from the permit date. For those pipe segments without apermit expiry date, random numbers between 0 and 45 are assigned using afield calculator in QGIS. Similar to this, based on the pipe grade informa-tion, the ultimate tensile strength (UTS) of pipe segments is estimated. Thestructure of the attribute table is modified using the refactor fields tool inQGIS once the cleaned shapefile data has been projected to the appropri-966.3. METHODOLOGY1. Data collectionCanadian Soil Information ServiceOil and Gas Pipe networkSoil texture, and bulk densityAlberta Energy RegulatorSoil pH and water contentISRIC World Soil Information2. Data cleaningData source Downloaded dataLoad pipe network and soil related parametersExtract transmission pipes from the network using QGIS expressionUse field calculator to estimate missing values and unit conversionClip soil information using Alberta shapefileSave all information as shapefile3. Rasterize vector data4. BBN Development5. Model integration (BBN & GIS)6. Vectorize raster outputRasterized dataBBN modelBnspatialRaster VectorExamplesFigure 6.2: Spatial Bayesian network flowchart for predicting the probabilitythat an oil and gas pipeline may fail due to external pitting corrosionate projection. Finally, the diameter, thickness, MAOP, UTS, coating, andpipe age image files are created using the rasterize tool in QGIS. The tooltransforms vector data into raster data so that the spatial BBN model canperform computations for each input raster grid cell. Before being used asan input in the BBN model, soil data including bulk density, soil ph, andwater content are used and processed in a similar manner. Details of the soilinformation is given in a subsequent section.The construction of a BBN model based on the corrosion model proposedby Velázquez et al. (2009), failure pressure model, and reliability model isthe next stage. The next section provides details about the BBN model. Ina subsequent step, the rasterized datasets and BBN model are integrated inRStudio using the bnspatial package (Figure 6.2). The output of the packageis a raster file, where each pixel represents the burst and leakage failureprobability of a pipeline due to pitting corrosion. The final step involvesgetting the output in raster form from the bnspatial package, convertingthe raster map into a vector, and getting and visualising the single failureprobability value for each pipe segment in a network. QGIS is used to carryout these tasks. The following steps are followed to determine the singlefailure probability for each pipe segment. The dataset for the cleaned pipenetwork is initially loaded, and a buffer is created. The system then uses976.3. METHODOLOGYa zonal statistics, which estimates the maximum value from the pixels thattouch a pipe segment, to compute the failure probability of each segment.The failure probability due to burst and leakage failure is indicated by theraster information. The pipe network’s attribute table is then updated toinclude the failure probability vector for burst and leakage. Finally, themaximum value of the burst and leakage failure probability is calculatedusing a field calculator. Next, QGIS displays the final failure probabilityresult.6.3.1 BBN model development for external corrosionThe BBN model for the failure analysis of oil and gas pipelines subjectedto external corrosion is shown in Figure 6.3. The three components of theBayesian network are the corrosion model, failure pressure model, and prob-ability of failure model. The corrosion model estimates the maximum depthand length of a defect that can be seen as a result of pitting corrosion. Thepredicted defect depth and length are then used to estimate a pipeline ca-pacity to handle burst pressure by using a failure pressure model. The prob-ability of failure (PoF) of a pipeline for a single corrosion defect was lastlydetermined in a reliability framework by coupling the failure pressure capac-ity with uncertain operating pressure (demand). The nodes in Figure 6.3stand in for stochastic variables, which affects probability of failure, whereasarrows show causal connections between the nodes. The causal structureis developed based on previous studies. Numerous random variables (suchas soil resistivity, chloride content, etc.) have continuous states, while theremainder have discrete states (such as coating type, soil type). The CPTfor each child node is calculated using data and empirical relationships thatare detailed in the following sections. The next section also explains thedetails of the corrosion model and how it relates to the failure pressure andreliability models. To determine the most sensitive parameters, sensitivityanalysis is then completed.Corrosion modelA power-law function (Equation 6.2) proposed by Velázquez et al. (2009)is used to predict the corrosion’s maximum defect depth. The pitting propor-tionality (coefficient) and exponent factors (exponent) are calculated usinga variety of soil and pipe parameters, under a constraint that each predictorvariable could only be connected to one factor (Equations 6.3-6.4). The soilrelated properties are redox potential (rp), pH (ph), pipe-to-soil potential986.3. METHODOLOGYCorrosion modelCorrosion DurationDefect depthProportionality Constant (K)Bicarbonate contentSulphate ContentChloride ContentpHRedox PotentialSoil ResistivityPitting Exponent (v)Water ContentCoating TypeBulk DensityPipe to Soil PotentialSoil TypePipe AgeCoating Failure TimeThicknessUltimate tensile strength (UTS) Failure pressureDefect lengthDiameterBurst pressure limit stateMean Operating Pressure (OP)Operating PressureCOV of OPFailure pressure modelReliability modelCorrosion durationCorrosion length rateFigure 6.3: BBN to analyze pipeline failure probability due to external cor-rosion(pp), soil resistivity (re), soil type (Class), water content (wc), soil bulkdensity (bd), dissolved chloride (cc), bicarbonate (bc), and sulfate (sc) ionconcentrations. The pipe related factors are age of pipeline (t) and coat-ing type (ct). Figure 6.4 displays the pairwise correlation of the corrosionvariables.dmax = k(t− t0)v (6.2)k = k0 + k1rp+ k2ph+ k3re+ k4cc+ k5bc+ k6sc (6.3)v = n0 + n1pp+ n2wc+ n3bd+ n4ct (6.4)where dmax is maximum corrosion defect depth (mm); k is coefficientof proportionality; v is pitting exponent; t0 is the coating failure time orpit initiation time; t is exposure time or pipe age. The values of k andv are computed using Equation 6.3 and 6.4, respectively. The k and ncoefficients are estimated using regression analysis depending on soil categoryas shown in Table 6.1 (Velázquez et al., 2009). The soil categories identifiedin Velázquez et al. (2009) study are clay, clay loam, and sandy clay loam, andall soil type. For a general soil type, Velázquez et al. (2009) recommends the996.3. METHODOLOGYdmaxtpHpprewcbdccbcscrp0.39 -0.28 0.32 -0.15 0.37 -0.32 0.24 -0.066 -0.041 -0.170.19 0.34 -0.094 0.27 -0.34 0.13 0.22 0.12 -0.100.013 -0.29 0.16 0.17 0.23 0.36 0.26 -0.27-0.18 0.21 -0.12 0.15 0.079 0.22 -0.25-0.47 -0.11 -0.12 -0.16 -0.15 0.33-0.17 0.20 0.12 0.21 -0.27-0.24 -0.090 0.12 -0.0670.61 0.23 -0.290.30 -0.27-0.20dmaxtpHpprewcbdccbcscrp-1.0-0.80-0.60-0.40-0.200.00.200.400.600.801.0Figure 6.4: Pairwise correlation of the corrosion variablesunbiased estimate of k and v are 0.164 and 0.780, respectively. In addition,the time required to initiate corrosion is taken as 2.88 years as well (Velázquezet al., 2009).Table 6.1: Regression coefficients by soil types (Velázquez et al., 2009).Coefficients Mixed Soil Clay Sandy Clay Loam Clay Loamt0 2.88 3.05 2.57 3.06k0 6.08E-01 5.51E-01 5.99E-01 9.84E-01k1 -1.80E-04 -8.98E-05 -1.82E-04 -1.06E-04k2 -6.54E-02 -5.90E-02 -6.42E-02 -1.15E-01k3 -2.60E-04 -2.15E-04 -2.12E-04 -2.99E-04k4 8.74E-04 8.38E-04 8.62E-04 1.80E-03k5 -6.39E-04 -1.28E-03 -6.78E-04 -4.88E-04k6 -1.22E-04 -5.33E-05 -1.13E-04 -2.09E-04n0 8.96E-01 8.85E-01 9.65E-01 2.82E-01n1 5.19E-01 4.93E-01 5.12E-01 4.61E-01n2 4.65E-03 3.72E-03 4.50E-03 1.69E-02n3 -9.91E-02 -1.01E-01 -1.58E-01 -9.87E-02n4 4.31E-01 4.67E-01 4.34E-01 5.67E-011006.3. METHODOLOGYA failure pressure model is used to estimate the burst pressure of apipeline for a single corrosion defect utilising the corrosion defect depth andlength parameters (Figure 6.3). The length of corrosion defect is estimatedusing the corrosion duration and corrosion length rate. Since there is no ev-idence to support a correlation between corrosion defect depth and length,a linear growth model is assumed to predict corrosion length as stated inEquation 6.5 (Mazumder et al., 2021; Chalgham et al., 2020). The corrosionduration is calculated as the difference between the pipe age and the timeuntil the coating fails or time of the last inspection.L(T ) = L0 + Va(T − Ti) (6.5)where L(T ) is length of corrosion pit; Ti is the time of the last inspection;Va is the axial corrosion growth rate; L0 is the initial length. A pipelineelapsed life up to the inspection date (15 years) is large enough to guaranteea linear corrosion growth rate (Va = L0/∆T , where ∆T is the pipelineelapsed time to inspection date). Discretization of the corrosion BBN modelis given in Table 6.2.1016.3. METHODOLOGYTable6.2:DiscretizednodesoftheBBNcorrosionmodel.ParametersSubcriteriaStatesDefectdepthPittingproportionality(k)ExtremelyLow0≤k<0.12Low0.12≤k<0.16Medium-High0.19≤k<0.25VeryHigh0.25≤k<1.12Pittingexponent(v)ExtremelyLow0≤v<0.61Low0.61≤v<0.69Medium-High0.77≤v<0.84VeryHigh0.84≤v<1.12Exposuretime(t)ExtremelyLow0≤t<12.5Low12.5≤t<20Medium-High35≤t<42.5VeryHigh42.5≤t<50Soiltype(ST)MixedSoilMSClayCContinuedonnextpage1026.3. METHODOLOGYTable6.2:DiscretizednodesoftheBBNcorrosionmodel.(Continued)ParametersSubcriteriaStatesSandyClayLoamSCLClayLoamCLProportionalitycoefficient(k)SoilpH(ph)ExtremelyLow4.14≤ph<5.09Low5.09≤ph<6.05Medium-High7.96≤ph<8.92VeryHigh8.92≤ph<11Soilresistivity(re)ExtremelyLow1.9≤re<68.17Low68.17≤re<134.43Medium-High266.97≤re<333.23VeryHigh333.23≤re<399.5Redoxpotential(rp)ExtremelyLow2.1≤rp<59.75Low59.75≤rp<117.4Medium-High232.7≤rp<290.35VeryHigh290.35≤rp<350Continuedonnextpage1036.3. METHODOLOGYTable6.2:DiscretizednodesoftheBBNcorrosionmodel.(Continued)ParametersSubcriteriaStatesChloridecontent(cc)ExtremelyLow0.99≤cc<61.17Low61.17≤cc<119.13Medium-High235.07≤cc<293.03VeryHigh293.03≤cc<351Bicarbonatecontent(bc)ExtremelyLow0.99≤bc<29.29Low29.29≤bc<54.67Medium-High105.44≤bc<130.82VeryHigh130.82≤bc<156.2Sulfatecontent(sc)ExtremelyLow0.99≤sc<230.51Low230.51≤sc<458.45Medium-High914.32≤sc<1142.26VeryHigh1142.26≤sc<1375Soiltype(ST)MixedSoilMSClayCContinuedonnextpage1046.3. METHODOLOGYTable6.2:DiscretizednodesoftheBBNcorrosionmodel.(Continued)ParametersSubcriteriaStatesSandyClayLoamSCLClayLoamCLPittingexponent(v)Soilbulkdensity(bd)ExtremelyLow1.11≤bd<1.18Low1.18≤bd<1.26Medium-High1.40≤bd<1.47VeryHigh1.47≤bd<1.55Pipetosoilpotential(pp)ExtremelyLow-1.97≤pp<-1.71Low-1.71≤pp<-1.45Medium-High-0.94≤pp<-0.68VeryHigh-0.68≤pp<-0.42Watercontent(wc)ExtremelyLow5≤wc<16.93Low16.93≤wc<25.07Medium-High41.33≤wc<49.47VeryHigh49.47≤wc<57.6Continuedonnextpage1056.3. METHODOLOGYTable6.2:DiscretizednodesoftheBBNcorrosionmodel.(Continued)ParametersSubcriteriaStatesCoatingtype(ct)Barepipe1Asphaltenamel0.9Wraptape0.8Coaltar0.7FBE0.3Soiltype(ST)MixedSoilMSClayCSandyClayLoamSCLClayLoamCLDefectlengthExposuretime(t)SimilarasaboveCorrosionlengthrateFixed31066.3. METHODOLOGYFailure pressure modelVarious standards, such as American Society of Mechanical Engineers(ASME) B31G (ASME, 1991) and Det Norske Veritas (DNV) RP-F101(DNV, 2004), are used to determine the failure pressure of metallic pipelinessubjected to corrosion. The various models take into account pipe inter-nal pressure and the shape of the corroded area. The performance of thestandards is evaluated by comparing the experimental burst failure pressurewith the pressure predicted from these standards. Mazumder et al. (2021)gathered a set of 92 experimental test results for oil and gas pipelines withcorrosion defects, where the database of burst failure tests is extracted fromliterature. The failure pressure of pipes determined by these standard codeswas then compared to experimental failure pressure determined by experi-ments by the authors. The ASME B31G technique produced a conservativeresult, and this can have significant economic consequences because it maycause pipelines to be needlessly renewed before their anticipated design life.On the other hand, the failure pressure prediction has the lowest variabilitywhen using the DNV RP-F101 method. In this study, the burst failure pres-sure of a pipeline subjected to external corrosion is estimated using the DNVmethod (Equation 6.6), and the result is used to determine the likelihood ofpipeline failure at a burst limit state.FP =2t× UTSD − wt[1− d(T )wt1− d(T )wt M−1](6.6)M =√1 + 0.31L(T )2Dwt(6.7)where UTS is ultimate tensile strength; D is diameter; wt is thickness;M is folias factor; d(T ) and L(T ) are the time dependent depth and lengthof corrosion defect, respectively. The corrosion defect depth at any giventime is calcualted from Equation 6.2. Discretization of the failure pressureBBN model is given in Table 6.3.Probability of failureThe probability of failure model determines the probability that a pipelinewill fail as a result of corrosion hazard. The failure can be due to burst orleakage. These failure modes are computed using the burst limit state func-tion (LSF1) and leakage limit state function (LSF2) because they are two1076.3. METHODOLOGYTable 6.3: Discretized nodes of the BBN burst pressure model.Parameters Sub criteria StatesFP Defect length (mm) Extremely Low 0≤ DL< 20Low 20≤ DL < 40- -High 500≤ DL <1000Very High 1000≤ DL < 1500Defect depth (mm) Extremely Low 0≤ DD < 0.43Low 0.43≤ DD < 0.69- -High 3.37≤ DD < 5Very High 5≤ DD < 20Thickness (mm) Extremely Low 2≤ Thickness< 4Low 4≤ Thickness< 5- -High 18≤ Thickness< 19Very High 19≤ Thickness< 30Diameter (mm) Extremely Low 80≤ Diameter< 350Low 350≤ Diameter< 500- -High 950≤ Diameter< 1100Very High 1100≤ Diameter< 1500UTS (MPa) Extremely Low 200≤ UTS< 300Low 300≤ UTS< 400- -High 600≤ UTS< 700Very High 700≤ UTS< 900independent events (Caleyo et al., 2002). The limit state functions are ex-pressed as follows:LSF1 = FP −OP (6.8)LSF2 = 0.8× wt− dmax (6.9)where FP is burst failure pressure, OP is the internal fluid pressure (op-erating pressure), and wt is pipeline wall thickness. The LSF1 is definedas the difference between the failure pressure and operating pressure. Theburst limit state is positive for safe corrosion defects (FP > OP ) and nega-1086.3. METHODOLOGYtive for failure defects (OP ≥ FP ). The BBN model for the LSF1 is shownin Fig 6.3, where each node represents the operating and failure pressure asoutlined in Equation 6.8. The operating pressure is modelled as a normaldistribution defined with mean (OPMn) and coefficient of variation (COV).Discretization of the BBN nodes is summarized in Table 6.4. The LSF2is defined as the difference between the pipeline wall thickness and depthof corrosion defect. This describes the probability of failure due to leak-age caused by defects with a depth close to corrosion allowance, which istypically 80% of wall thickness (Caleyo et al., 2002). While it is importantto remember that the occurrence of defects with depths greater than thecorrosion allowance does not necessarily indicate leaking, this is nonethelessregarded in the petroleum sector as a major integrity issue.The occurrence of either of these two failure events is generally viewedas a failure of the pipe. Thus, the probability of failure due to corrosion willbe determined by which has a larger probability (Chalgham et al., 2020):PoF = max(LSF1, LSF2) (6.10)Finally, for simplicity and representation purposes, the output is groupedinto low (0-0.1), medium (0.1-0.4), and high (0.4-1.0) probability of failure(Shabarchin and Tesfamariam, 2016).Sensitivity analysisSensitivity analysis is used to assess how sensitively the probability ofthe query node changed as the input variables changed (Guo et al., 2020b).Additionally, it helped to prioritise the collecting of new data by helpingto identify important uncertainty (Tesfamariam and Martín-Pérez, 2008).Several approaches, including entropy reduction, variance reduction, andvariance of beliefs estimations, have been developed to perform sensitivityanalysis in a Bayesian network (Pearl, 1988). The variance reduction methodis used to assess the sensitivity of the output of the BBN model to variationin a particular input parameter because the input parameters needed toassess the output of the BBN model (maximum pit depth, burst failure, andleakage failure) have discrete and continuous values. The method computesthe variance reduction of the expected real value of a query node Q due toa finding at a varying variable node F . As a result, the variance of the realvalue of Q given evidence F , V (q|f), is computed as (Pearl, 1988; Saltelliet al., 2010):V (q|f) =∑qp(q|f)[Xq − E(Q|f)]2 (6.11)1096.3. METHODOLOGYTable 6.4: Discretized nodes of the failure probability BBN model.Parameters Sub criteria StatesOP OPMn (MPa) Extremely Low 0≤ OPMn< 4Low 4≤ OPMn < 6- -High 28≤ OPMn < 32Very High 32≤ OPMn < 35COV Fixed 0.05LSF1 OP (MPa) Extremely Low 0≤ OP< 4Low 4≤ OP < 6- -High 28≤ OP < 32Very High 32≤ OP < 35FP (MPa) Extremely Low 0≤ FP< 4Low 4≤ FP < 6- -High 28≤ FP < 32Very High 32≤ FP< 35where, f is the state of varying node F ; q is the state of the query node Q;p(q|f) is the conditional probability of q when node F is given to be in statef ; Xq is the numeric value corresponding to state q; E(Q|f) is the expectedreal value of Q due to a finding of the state f in node F . To estimatethe degree of influence on the outcome, Netica varies parent nodes (varyingnodes), with no prior assumptions made regarding distribution function ofthe inputs. Based on this degree of influence, nodes in the model are rankedaccordingly.6.3.2 Case study: Province of Alberta’s oil and gas pipelinenetworkThe proposed methodology is applied to the oil and gas database col-lected from the website of Alberta Energy Regulator. The network comprisesaround 316,079 pipes of different status (e.g., operating, abandoned, discon-tinued, permitted, and removed), substance being transported (e.g., naturalgas, fuel gas, crude oil, and others), and pipe characteristics (e.g., diameter,material, wall thickness, grade, maximum operating pressure, and others).A pipe segment is regarded in this study as one that has a unique GIS ID.1106.3. METHODOLOGYIn this study, transmission pipelines for oil and natural gas with outside di-ameters of 323.9 mm or greater and maximum operating pressures of 3475kPa or greater are taken into consideration for further analysis. Addition-ally, the pipelines are filtered based on operation status and buried pipelines.Pipelines which lies above ground are specified as surface line crossing in thedataset and, hence, pipelines not meeting this requirement are assumed asburied pipeline. Figure 6.5 displays the resulting pipeline network for bothnatural gas and oil.Pipeline networkCrude OilNatural GasAlbertaFigure 6.5: Alberta’s transmission pipeline system for oil and gas.The network’s pipe segments range in age from 0 to 45 years (Figure6.6a). The pipe segments have different operating pressure that rangesfrom 3.5 to 22.75 MPa (Figure 6.6b). According to Figures 6.6c and d,the pipes’ diameter and thickness range from 406.4mm to 1219.2mm and4mm to 25.4mm, respectively. The majority of pipes have ultimate tensilestrengths between 330 and 915 MPa (Figure 6.6e). Finally, the pipes are ei-ther uncoated or coated with FBE (Figure 6.6f). The histogram distributionfor these pipe parameters is given in Figure 6.7.Additionally, according to the categories listed in Velázquez et al. (2009),1116.3. METHODOLOGYPipe ageBand 1 (Gray)AlbertaOperating pressureBand 1 (Gray)AlbertaDiameterBand 1 (Gray)AlbertaThicknessBand 1 (Gray)AlbertaUTSBand 1 (Gray)AlbertaCoatingFBEBare pipeAlbertaFigure 6.6: Pipe characteristics, including (a) Pipeline age , (b) Maximumoperating pressure , (c) Pipe diameter, (d) Pipe thickness, (e) Ultimatetensile strength, (f) coating type.1126.3. METHODOLOGY0 5 10 15 20 25 30 35 40050100150200250300 Co un tPipe age (Years)5 10 15 20 250100200300400500600700800 Co un tOperating pressure (MPa)02004006008001000Co un t406.4 457.2 558.8 609.6 660.4 863.6 914.4 1066.8 1219.2Diameter (mm)3 8 13 18 235 10 15 20 25020040060080010001200 Co un tThickness (mm)330 414 415 455 482 489 490 514 517 531 560 565 620 758 760 915020040060080010001200 Co un tUltimate tensile strength (MPa)Bare pipe FBE050010001500200025003000 Co un tCoating typeFigure 6.7: Histogram for (a) Pipeline age , (b) Maximum operating pressure, (c) Pipe diameter, (d) Pipe thickness, (e) Ultimate tensile strength, (f)coating type.the soil types under which pipes are buried are classified as clay, clay loam,sandy clay loam, and mixed soil (Figure 6.8a). The data is obtained from Soil1136.4. RESULT AND DISCUSSIONLandscapes of Canada (SLC), in terms of percentage of total sand, silt, andclay by weight, which was developed by Agriculture and Agri-Food Canadato provide information on the country’s agricultural soils at the provincialand national levels. The soil texture for the region is categorized by plottingover the ternary diagram generated using a Matlab script prepared by HolgerHoffmann (2021). Most soil samples comprise clay, silt, and loam or anycombination of those three. The bulk density of the soil is obtained fromSLC. Figure 6.8b shows that the bulk density of soil ranges from 0.5 to2g/cm3. The soil moisture or water content and soil ph are obtained fromthe ISRIC-WISE (Global Data Set of Derived Soil Properties, 0.5-DegreeGrid) (Batjes, 2000) database. The study area’s overall water content rangesfrom 22.5% to 90% (Figure 6.8c). The soil ph is found to vary from 2.27 to9.10 (Figure 6.8d).6.4 Result and discussionUsing the empirical equations Velázquez et al. (2009) has proposed, thecorrosion BBN model is developed. The equal interval, equal quantile, andmoment matching approaches are used to discretize the continuous variablesin the BBN model. The Bayesian network may represent continuous datamore effectively with more intervals, but as the number of intervals increases,so does the size of the CPT for each node. As a result, in the current study,the number of intervals for the input nodes, in both the equal interval andequal quantile techniques, is restricted to 6. In moment matching methods,the equations used to calculate the break points increases as more breakpoints are present (2∗n−1). The problem becomes more challenging to solveas the number of equations increases and calls for an optimization solution.Nojavan A et al. (2017) stated that the problem becomes intractable oncethere are more than four break points. The moment matching is constrainedto 3 number of intervals or 4 number of break points as a result of thisdifficulty. The entire corrosion database is applied to the three discretiza-tion methods-based corrosion models that have been created, and Figure 6.9displays the models’ predictions. The results of the equal interval (73%)and equal quantile (74%) discretization approaches are nearly identical. Be-cause the moment matching approach employs fewer intervals than the othermethods, its prediction accuracy (69%) is a little lower than expected. Giventhe potential of the moment matching method, additional research into thismethod and other advanced discretization techniques can be done in thefuture to improve the model’s ability to predict outcomes accurately.1146.4. RESULT AND DISCUSSIONSoil typeBand 1 (Gray)Mixed SoilClaySandy Clay LoamClay LoamAlbertaBulk densityBand 1 (Gray)AlbertaWater contentBand 1 (Gray)AlbertaSoil phBand 1 (Gray)AlbertaFigure 6.8: Soil characteristics (a) Soil type , (b) Bulk density , (c) Watercontent, (d) Soil pH.The Velázquez et al. (2009) corrosion dataset is also used to train andvalidate the corrosion BBN model for comparison. The dataset is split intotraining and testing datasets, with each having 80% and 20%, respectively,of the total database. The corrosion model was trained using the trainingdata, and its ability to apply to new datasets was verified using the test-ing data. Using the equal quantile method, the corrosion model discretized1156.4. RESULT AND DISCUSSION1.57 50 9.88 ‐0.76 41 25.7 1.21 234 52.070.91 11 6.18 ‐1.43 18.7 19.4 1.34 8.8 16.650.94 18 6.09 ‐0.86 20.6 19.9 1.28 18.27 22.190.79 23 6.55 ‐1.03 30.9 21.4 1.17 351 91.120.94 20 6.01 ‐0.91 24.1 22.6 1.19 7.92 11.10.66 19 7.55 ‐0.71 14 29.9 1.38 15.85 13.871.78 33 5.42 ‐0.69 29.5 25.7 1.25 11.7 6.511.91 15 5.32 ‐1.07 14.1 29 1.37 26.06 8.320.79 19 5.89 ‐0.77 5.7 27.7 1.44 17.58 25.017.59 37 5.49 ‐0.82 22.3 31.8 1.23 117 7.810.94 20 6.01 ‐0.78 85.8 20.1 1.22 12.83 16.651.17 15 6.42 ‐0.9 34.5 24.1 1.3 33.52 11.10.81 13 7.11 ‐0.6 25 19.4 1.27 48.12 9.240.79 21 7.62 ‐0.93 32 16.2 1.37 8.27 16.650.76 21 5.73 ‐0.79 81.1 12.5 1.29 9.93 19.420.76 22 6.4 ‐0.88 30.9 18.8 1.24 8.27 5.552.21 22 5.2 ‐0.87 98.5 21 1.29 12.33 16.654.15 16 4.99 ‐0.6 30 18.9 1.33 100.8 5.550.79 23 8.01 ‐0.96 44.9 24.2 1.25 263.2 78.11.14 33 6.24 ‐0.71 36.3 19.2 1.26 46.25 5.210.99 11 6.5 ‐0.9 55.9 28.3 1.4 18.2 8.32R² = 0.73012456789100 1 2 3 4 5 6 7 8 9 10P re di c te d Pi t d ep th ( mm)Observed Pit depth (mm)(a)50 9.88 ‐0.76 41 25.7 1.21 234 52.0711 6.18 ‐1.43 18.7 19.4 1.34 8.8 16.6518 6.09 ‐0.86 20.6 19.9 1.28 18.27 22.193 6.55 ‐1.03 30.9 21.4 1.17 351 91.1220 6.01 ‐0.91 24.1 22.6 1.19 7.92 11.119 7.55 ‐0.71 14 29.9 1.38 15.85 13.8733 5.42 ‐0.69 29.5 25.7 1.25 11.7 6.5115 5.32 ‐1.07 14.1 29 1.37 26.06 8.329 5.89 ‐0.77 5.7 27.7 1.44 17.58 25.0137 5.49 ‐0.82 22.3 31.8 1.23 117 7.8120 6.01 ‐0.78 85.8 20.1 1.22 12.83 16.655 6.42 ‐0.9 34.5 24.1 1.3 33.52 11.113 7.11 ‐0.6 25 19.4 1.27 48.12 9.2421 7.62 ‐0.93 32 16.2 1.37 8.27 16.651 5.73 ‐0.79 81.1 12.5 1.29 9.93 19.4222 6.4 ‐0.88 30.9 18.8 1.24 8.27 5.5522 5.2 ‐0.87 98.5 21 1.29 12.33 16.6516 4.99 ‐0.6 30 18.9 1.33 100.8 5.5523 8.01 ‐0.96 44.9 24.2 1.25 263.2 78.133 6.24 ‐0.71 36.3 19.2 1.26 46.25 5.2111 6.5 ‐0.9 55.9 28.3 1.4 18.2 8.32R² = 0.74012456789100 1 2 3 4 5 6 7 8 9 10P re di c te d Pi t d ep th ( mm)Observed Pit depth (mm)(b)1.57 50 9.88 ‐0.76 41 25.7 1.21 234 52.070.91 11 6.18 ‐1.43 18.7 19.4 1.34 8.8 16.650.94 18 6.09 ‐0.86 20.6 19.9 1.28 18.27 22.190.79 23 6.55 ‐1.03 30.9 21.4 1.17 351 91.120.94 20 6.01 ‐0.91 24.1 22.6 1.19 7.92 11.10.66 19 7.55 ‐0.71 14 29.9 1.38 15.85 13.871.78 33 5.42 ‐0.69 29.5 25.7 1.25 11.7 6.511.91 15 5.32 ‐1.07 14.1 29 1.37 26.06 8.320.79 19 5.89 ‐0.77 5.7 27.7 1.44 17.58 25.017.59 37 5.49 ‐0.82 22.3 31.8 1.23 117 7.810.94 20 6.01 ‐0.78 85.8 20.1 1.22 12.83 16.651.17 15 6.42 ‐0.9 34.5 24.1 1.3 33.52 11.10.81 13 7.11 ‐0.6 25 19.4 1.27 48.12 9.240.79 21 7.62 ‐0.93 32 16.2 1.37 8.27 16.650.76 21 5.73 ‐0.79 81.1 12.5 1.29 9.93 19.420.76 22 6.4 ‐0.88 30.9 18.8 1.24 8.27 5.552.21 22 5.2 ‐0.87 98.5 21 1.29 12.33 16.654.15 16 4.99 ‐0.6 30 18.9 1.33 100.8 5.550.79 23 8.01 ‐0.96 44.9 24.2 1.25 263.2 78.11.14 33 6.24 ‐0.71 36.3 19.2 1.26 46.25 5.210.99 11 6.5 ‐0.9 55.9 28.3 1.4 18.2 8.321.68 19 5.3 ‐1.06 107.7 20 1.37 4.4 11.1R² = 0.69012456789100 1 2 3 4 5 6 7 8 9 10P re di c te d Pi t d ep th ( mm)Observed Pit depth (mm)(c)Figure 6.9: BBN model prediction for corrosion defect depth using differentdiscretization methods: (a) Equal interval, (b) Equal quantile and (c) Mo-ment matchingthe continuous variables from the training dataset. The training (88%) andtesting (81%) accuracy of the dataset are shown in Figure 6.10. In boththe training and testing phases, the predicted corrosion defects are fairlydistributed along a line of equality. The model’s ability to predict corrosiondefect depth is validated by the relatively consistent and highly accurateresults of the training and testing datasets. This provides a greater pre-diction accuracy in comparison to the earlier corrosion results. However,1166.4. RESULT AND DISCUSSIONthe corrosion model created using the database is seen to have some draw-backs. First, the corrosion model predicts a high defect depth for a pipelinewith an exposure period less than 5 years, and this is because the corro-sion database has information on pipelines with a minimum exposure timeof five years. Second, when compared to the corrosion model created usingempirical equations, the data-based model demonstrated less sensitivity tothe input nodes. As a result, the empirical equations are chosen over thedatabase in the current study to create the corrosion BBN model.2 1.96 23 7.95 ‐0.98 44.3 29.3 1.163 0.41 15 6.48 ‐1.05 161.2 16.6 1.274 0.41 12 7.45 ‐0.86 149.1 14.3 1.415 1.57 22 5.02 ‐0.96 72 20.3 1.256 7.04 8 5.08 ‐0.45 3.8 27.3 1.227 4.75 27 5.98 ‐0.7 51.3 29.7 1.318 0.99 21 6.16 ‐0.96 28.4 19.5 1.359 0.79 21 6.14 ‐0.92 25.8 16.1 1.4310 1.19 39 7.8 ‐0.95 15.4 38.6 1.3911 1.57 20 5.65 ‐0.83 19.3 27 1.2512 0.81 27 6.91 ‐0.85 14.6 25.1 1.2713 1.27 26 6.52 ‐0.83 17.8 28.4 1.3214 1.04 26 6.89 ‐0.8 15.6 23.9 1.3315 3.91 21 4.86 ‐0.8 89.6 16.1 1.2616 1.14 33 5.04 ‐0.7 143.8 19.3 1.1817 1.17 21 6.27 ‐0.99 55.2 16.4 1.2818 0.79 19 5.86 ‐0.77 10.4 27 1.4419 7.59 35 6.33 ‐0.57 11.4 24.9 1.1720 0.51 21 6.4 ‐0.88 30.9 18.8 1.4321 0.43 12 6.85 ‐1.08 45.1 17.2 1.3322 1.27 23 4.6 ‐0.8 129.3 20.8 1.2723 1.57 50 9.88 ‐0.76 41 25.7 1.21R² = 0.880123456789100 1 2 3 4 5 6 7 8 9 10P re di c te d Pi t d ep th ( mm)Observed Pit depth (mm)(a)11.7 65.09 146.1 125 Wrap_Tape Clay9.93 19.42 93.05 274 Wrap_Tape Clay54.58 8.32 36.28 50 FBE Sandy_Clay_Loam12.49 16.55 172.3 180 Wrap_Tape Clay47.57 8.32 14.9 95 Bare_Pipe Clay146.2 104.1 59.98 41 Coal_Tar Clay_Loam10.75 13.87 41.41 298 Wrap_Tape Sandy_Clay_Loam18.2 16.65 126.9 312 Coal_Tar Sandy_Clay_Loam3.2 13.89 145.3 144 Coal_Tar Sandy_Clay_Loam15.44 11.1 34.3 340 Wrap_Tape Clay117 65.09 43.23 89 Coal_Tar Clay_Loam20.4 19.42 77.33 261.4 Coal_Tar Clay_Loam17.11 11.1 75.01 271.3 Coal_Tar Clay_Loam56.32 5.55 34.8 189 Bare_Pipe Clay17.75 3.91 191.5 2.1 Coal_Tar Clay8.82 12.93 93.58 106 Wrap_Tape Clay3.2 25.01 259.3 25 Wrap_Tape Sandy_Clay_Loam58.5 6.51 43.23 189 Coal_Tar Clay8.27 14.76 80.1 202 Wrap_Tape Sandy_Clay_Loam4.4 11.1 100.7 78 FBE Clay_Loam8.8 9.71 98.1 130 Coal_Tar ClayR² = 0.810123456789100 1 2 3 4 5 6 7 8 9 10P re di c te d Pi t d ep th ( mm)Observed Pit depth (mm)(b)Figure 6.10: BBN model prediction for corrosion defect depth: (a) Trainingdataset and (b) Testing datasetThe failure pressure model is used to calculate the failure pressure capac-ity of each pipeline segment using the predicted defect depth and length asinputs. The authors’ earlier publication, validates and presents the failurepressure section of the BBN model (Tesfamariam et al., 2021). Figure 6.11illustrates a comparison of the predicted BBN failure pressure and experi-mental failure pressure with a high coefficient of determination (R2 = 0.91).The failure pressure capacity and operating pressure were coupled in areliability framework to evaluate the failure probability due to burst. Sim-ilar to this, the thickness of the pipe wall and the expected depth of thecorrosion defect were used to calculate the probability that a pipeline willfail due to a leak. Additionally, the effects of the discretization techniqueson failure probability are compared. For the assessment, the pipeline 1 fea-1176.4. RESULT AND DISCUSSIONFigure 6.11: Comparison of the experimental test failure pressure with theBBN model predicted failure pressure, obtained from Tesfamariam et al.Tesfamariam et al. (2021)tures listed in Table 6.5 are assumed. Furthermore, mixed soil and bare pipeare considered as the respective soil type and coating type. The burst limitstate is calculated from the probabilistic distribution of operating and failurepressures. For each case, the pipe age was varied from 0 to 45 years, andcorresponding probability of failure, i.e., exeedance of the burst limit state(LSF1) is computed. Fragility curves is used to represent the probabilityof exceeding the burst limit state function with time (Tesfamariam et al.,2021; Shabarchin and Tesfamariam, 2017). Lognormal distribution fragilitycurves are fitted to the probability of failure, and the result is plotted in Fig-ure 6.12. Similar approach followed to calculate the leakage limit state. Thedashed line displays the fragility curve for the leak failure, while the solidline displays the curve for the burst failure. The results show that in thefirst thirty to thirty-five years of a pipeline, the likelihood of leak failure ishigher than the likely of burst failure, and thereafter the likelihood of burstfailure increases. Additionally, the difference between the fragility curvesfor rupture and leak shows how the discretization of input node affect theoutput. For further study, the corrosion model created using equal quantilediscretization was chosen due to its higher corrosion defect depth predictionaccuracy in comparison to the others (Figure 6.9) and conservative output(Figure 6.12).The results of sensitivity analysis for the BBN model is described in1186.4. RESULT AND DISCUSSIONTable 6.5: Pipeline characteristics for assessing failure probability.Pipe characteristics Pipeline 1 Pipeline 2Diameter (mm) 323.9 609.6Thickness (mm) 6.4 10.3UTS (MPa) 455 565Operating Pressure (MPa) 7.24 9.9300.10.20.30.40.50.60.70.80.910 5 10 15 20 25 30 35 40 45P ro ba bi l it yTime (years)Discretization method = Equal interval (Burst)Discretization method = Equal quantile (Burst)Discretization method = Moment matching (Burst)Discretization method = Equal interval (Leak)Discretization method = Equal quantile (Leak)Discretization method = Moment matching (Leak)Figure 6.12: Changes in the burst and leak failure probabilities for differentdiscretization techniques.Figure 6.13 for maximum pit depth, burst failure, and leakage failure. Thefigures display the variance reduction percentage for the parent node or inde-pendent input parameters. The parameters that have highest contributionsin predicting maximum pit depth are pipe to soil potential (9.31%), chloridecontent (7.45%), pH (6.65%), coating type (6.2%), pipe age (4.48%), andwater content (2.29%). To a lesser degree, sulfate content (0.775%), bicar-bonate content (0.725%), soil resistivity (0.418%), redox potential (0.182%),soil bulk density (0.09%), and soil type (0.08%) shows sensitivities as well.The sensitivity result of equal interval and moment matching discretizationtechniques are provided in Figure 6.13a for a comparison. The variablesthat have highest contributions in the burst failure probability estimationare operating pressure (21.3%), thickness (9.1%), diameter (8.79%), andUTS (4.42%). To a lesser degree, pipe age and pipe to soil potential alsocontributes 0.17%, and 0.10%, respectively. The factors that have highestcontributions in predicting leakage failure probability are pipe to soil po-tential (2.06%), pipe wall thickness (1.69%), pH (1.47%), chloride content1196.4. RESULT AND DISCUSSION(1.41%), coating type (1.35%), and pipe age (1.02%). To a lesser degreewater content (0.84%), soil type (0.46%), soil bulk density (0.28%), sulfatecontent (0.19%), and bicarbonate content (0.11%) show sensitivities as well.pppHtccctwcSTscbcrerpbd0 2 4 6 8 10 12 14 16 18 20 22 24Percent (%)B BN no de s Moment matching Equal quantile Equal interval(a)Operating pressureThicknessDiameterUTStpppHctccwcSTbdscbcrerp0 5 10 15 20Percent (%)B BN no de s Leakage Burst(b)Figure 6.13: Sensitivity analysis of the BBN model for: (a) Maximum pitdepth and (b) Failure probabilitiesThe most sensitive parameters for the burst and leak failure, accordingto the sensitivity analysis, are those related to the pipe (such as operatingpressure, pipe wall thickness, diameter, UTS, coating type, and pipe age),or those related to the soil (such as pipe to soil potential, pH, and chloridecontent). As a result, a parametric analysis is carried out to evaluate themodel’s performance by changing a few important model parameters. In thesensitivity analysis, two pipelines with various characteristics are taken intoaccount to represent the sensitive pipe-related parameters (Table 6.5). Inaddition, two cases are considered; one each accounting for soil type, andcoating type, respectively. The discrete values used for the two cases are:Case 1 soil type = Mixed Soil, Clay, Sandy Clay Loam, Clay Loam; Case 2coating type = Bare pipe, Asphalt enamel, Wrap tape, Coal tar, FBE. Forthe two pipe material in Table 6.5, values for each case are varied by keepingST = Mixed Soil, and ct = Bare pipe. The fragility curves are producedsimilarly to how it was previously stated.The parametric study of Case 1 shows the fragility curves of four soiltypes for both burst and leak failure (Figure 6.14). The pipeline 1 fragilitycurve is shown by the solid line, while the pipeline 2’s curve is shown by the1206.4. RESULT AND DISCUSSIONdashed line. The overall conclusion shows that pipeline 1 is more likely toburst and leak than pipeline 2 for all soil types. Although it is challengingto generalize, the results suggest that pipe segments with a high diameterhave a lower probability of failure throughout the duration and are there-fore safe to operate in comparison to pipe segments with a small diameter.This can be explained by the fact that pipes with smaller sizes have thinnerwalls than pipes with greater diameters. Operators and regulatory bodiesshould pay particular attention to these pipelines, setting up suitable moni-toring and preventive measures, in order to eliminate failure (Shabarchin andTesfamariam, 2016). Additionally, how soil types affect the failure proba-bility may be seen in the difference between the fragility curves for ruptureand leak. Based on their tendency for pitting corrosion, the soil types areclassified as clay, mixed soil, clay loam, and sandy clay loam. As anticipated,compared to other soil types, a pipeline buried in sandy soil type fails moreslowly from external pitting corrosion.00.10.20.30.40.50.60.70.80.910 5 10 15 20 25 30 35 40 45P ro ba bi l it yTime (years)Soil Type = Mixed soil (Pipe 1)Soil Type = Clay (Pipe 1)Soil Type = Sandy clay loam (Pipe 1)Soil Type = Clay loam (Pipe 1)Soil Type = Mixed soil (Pipe 2)Soil Type = Clay (Pipe 2)Soil Type = Sandy clay loam (Pipe 2)Soil Type = Clay loam (Pipe 2)P ro ba bi l it y(a)00.10.20.30.40.50.60.70.80.910 5 10 15 20 25 30 35 40 45P ro ba bi l it yTime (years)Soil Type = Mixed soil (Pipe 1)Soil Type = Clay (Pipe 1)Soil Type = Sandy clay loam (Pipe 1)Soil Type = Clay loam (Pipe 1)Soil Type = Mixed soil (Pipe 2)Soil Type = Clay (Pipe 2)Soil Type = Sandy clay loam (Pipe 2)Soil Type = Clay loam (Pipe 2)(b)Figure 6.14: Fragility curves for variation in soil types: (a) Burst failure and(b) Leakage failureThe parametric study of Case 2 shows the fragility curves of five pipecoating types for both burst and leak failure (Figure 6.15). The solid lineand dashed line, respectively, represent the pipelines 1 and 2 fragility curves.The overall finding suggests that pipeline steel is well-protected by an intactcoating (like FBE). Compared to other coating types, a pipeline with FBEcoating type takes longer time to fail from external pitting corrosion. Incomparison to FBE coating, the fragility curves of a pipeline covered in coaltar, wrap tape, and asphalt enamel all show a trend toward increased fragility1216.4. RESULT AND DISCUSSIONover time. In the absence of a coating, the likelihood of a pipeline burstingor leaking due to external corrosion is significant (e.g., bare pipe). Finally,the difference in failure probability between pipelines 1 and 2 can be noticedhere as well and is understandable for the reasons mentioned previously.00.10.20.30.40.50.60.70.80.910 5 10 15 20 25 30 35 40 45P ro ba bi l it yTime (years)Coating = Bare Pipe (Pipe 1)Coating = Asphalt enamel (Pipe 1)Coating = Wrap Tape (Pipe 1)Coating = Coal Tar (Pipe 1)Coating = FBE (Pipe 1)Coating = Bare Pipe (Pipe 2)Coating = Asphalt enamel (Pipe 2)Coating = Wrap Tape (Pipe 2)Coating = Coal Tar (Pipe 2)Coating = FBE (Pipe 2)(a)00.10.20.30.40.50.60.70.80.910 5 10 15 20 25 30 35 40 45P ro ba bi l it yTime (years)Coating = Bare Pipe (Pipe 1)Coating = Asphalt enamel (Pipe 1)Coating = Wrap Tape (Pipe 1)Coating = Coal Tar (Pipe 1)Coating = FBE (Pipe 1)Coating = Bare Pipe (Pipe 2)Coating = Asphalt enamel (Pipe 2)Coating = Wrap Tape (Pipe 2)Coating = Coal Tar (Pipe 2)Coating = FBE (Pipe 2)(b)Figure 6.15: Fragility curves for variation in pipe coating types: (a) Burstfailure and (b) Leakage failureFinally, in order to facilitate failure mitigation programs and enhanceresource allocation strategies, the burst and leakage failure probability ofthe Alberta oil and gas transmission pipeline is produced using the freelyaccessible GIS software QGIS. The pipe and soil properties listed in Figures6.6 and 6.8 serve as the input data. The BBN model calculates the missingdata (such as pipe to soil potential, redox potential, chloride content, bicar-bonate content, sulfate content, and soil resistivity) based on the soil type,where each variable has a statistical distribution according on the soil typeas provided by Caleyo et al. (2009b). The resulting failure probability ofthe pipe segments due to burst and leak is shown in Figure 6.16. Accordingto their likelihood of failure, pipe segments can be divided into failure cate-gories for the purpose of simplicity and representation: low (0-0.1), medium(0.1-0.4), and high (0.4-1.0). High failure probability pipeline segments re-quire an intervention for maintenance or repair activities. If sufficient care isnot taken to the pipes with a medium failure probability, they could degradequickly and increase their failure probability. Decision makers can tune thethresholds depending on the location and condition of pipeline.The maximum value of burst and leakage failures is used to calculate thefinal failure probability for each pipe section (Figure 6.17). The percentage of1226.4. RESULT AND DISCUSSIONBurst failure probability0 - 0.10.1 - 0.20.2 - 0.30.3 - 0.40.4 - 0.50.5 - 0.60.6 - 0.70.7 - 0.80.8 - 0.90.9 - 1Alberta (a)Leakage failure probability0 - 0.10.1 - 0.20.2 - 0.30.3 - 0.40.4 - 0.50.5 - 0.60.6 - 0.70.7 - 0.80.8 - 0.90.9 - 1Alberta (b)Figure 6.16: Failure probability of oil and gas pipelines due to: (a) Burstand (b) Leakagepipeline segments that fall into the failure categories are:low (47%), medium(25%), and high (28%). The majority of the pipe segments have a low failureprobability, followed by a high failure probability. The latter can be primarilybecause of the coating; most pipe segments lack information on their coatingsand are therefore thought to be bare pipes. As stated previously, pipelinefailure becomes more likely in the absence of a coating. Additionally, bycomparing the final failure probability with the burst and leakage failureprobability in Figure 6.16, it can be seen that the burst failure probabilityis a more common failure type. Figure 6.18 shows the distribution of thelikelihood that the pipeline segments in the system will fail due to a burst,leak, or maximum of the two. According to the fittings’ mean values, thelikelihood of the pipeline system’s failing due to a burst is higher than theleakage. For the pipe segments in the system, the average failure probabilitycaused by external corrosion is 0.248. Finally, using a complete pipelineinfrastructure data as well as soil data would improve this analysis and makeit more accurate, helping to better identify which pipeline segments needinspection and maintenance.A pipeline generally does not have a constant risk potential along itswhole length. As the environment around the pipeline’s route changes, sodoes the pipe’s risk state. Pipelines can be effectively examined in shortersections by segmenting them, and the results of each segment’s risk assess-1236.5. SUMMARYProbability of failure0 - 0.10.1 - 0.20.2 - 0.30.3 - 0.40.4 - 0.50.5 - 0.60.6 - 0.70.7 - 0.80.8 - 0.90.9 - 1Alberta Figure 6.17: Failure probability of oil and gas pipelines due to externalpitting corrosion.ment will differ. Although it has the benefit of improving assessment accu-racy for each segment, it could also lead to increased data expenses. Longersections, however, may lower data costs but potentially lower accuracy. Inliterature, dynamic segmentation is referred to as an appropriate segmenta-tion technique that introduces a break point whenever a major shift in riskoccurs (e.g., wall thickness, diameter, soil pH, moisture, population densityand other risk variables). The suggested approach, which uses rasterizedspatial data to model failure of a vast network of pipes buried beneath vari-ous environments, provides the same benefit as segmentation. However, thequality of the provided spatial data determines how accurate the analysiswill be (high resolution).6.5 SummaryAn integrated BBN-GIS model has been developed in this study to cal-culate the likelihood that oil and gas pipelines exposed to external pitting1246.5. SUMMARY0.0 0.2 0.4 0.6 0.802004006008001000120014001600Co un tFailure probability Burst failure probability Leak failure probability Probability of failureNormalmu sigmaB_max 0.21973 0.27852L_max 0.12528 0.17477PoF 0.24885 0.27224Figure 6.18: Failure probability distribution for the pipeline segments in thesystem.corrosion will fail. Corrosion, failure pressure, and reliability models are allintegrated in the BBN model. The corrosion model predicts the depth andlength of the corrosion defects. These variables are then utilized to calculatethe pipe segment’s failure pressure capacity. In order to determine the failureprobability due to burst, the failure pressure capacity and operating pres-sure were combined in a reliability framework. Similar to this, the estimateddepth of the corrosion defect and the thickness of the pipe wall were usedto estimate the likelihood of a pipeline failing due to a leak. The pipelinefailure probability due to external pitting corrosion is then calculated as themaximum of the leakage and burst failure probabilities.The benefits of developing a corrosion model utilizing empirical equa-tions and corrosion dataset were thoroughly discussed. When creating thecorrosion BBN model, empirical equations are chosen over corrosion datasetssince the latter predicts high corrosion defect depth for pipelines exposed forless than 5 years and has less sensitivity to the input nodes. Additionally, theusefulness of three distinct discretization techniques—equal interval, equalquantile, and moment matching—was also explored. Their influence on thefailure probability is evaluated using examples. The failure pressure model1256.5. SUMMARYwas developed using the DNV equation. In a previous study, the authorsused a database of burst failure tests that had been compiled by Mazumderet al. (2021) to validate the failure pressure model. The fact that a high co-efficient of determination (R2 = 0.91) was attained suggests that BBN canaccurately estimate the burst failure pressure as well. The failure probabilityfor Alberta’s oil and gas transmission pipelines is also computed by the BBNmodel. The final results show that more pipe segments may fail due to burstrather than leak when there is an external pitting corrosion. Additionally,the majority of pipe segments have low and high failure probabilities.The current study has some limitations. The proposed BBN-GIS modelcan be useful to calculate the likelihood that a network’s pipelines will fail.However, as there are many missing values in the pipe and environmentaldata needed to create the model, it will need to be further validated usingaccurate and full data gathered from pipeline operators. For example, dueto a lack of coating information, the majority of the pipes in the currentstudy are assumed to be bare pipes. Future research can focus on gatheringthis data and connecting it to the already identified coating types. Theinteraction of independent corrosion defects was not taken into account inthe current study, and the failure probability of each pipeline segment ispredicted based on the single maximum defect depth that can develop on aburied pipeline exposed for a number of years to the soil environment. Thecausal structure identified by Velázquez et al. (2009) served as the foundationfor the development of the BBN model. By creating the corrosion modelbased on the most sensitive parameters and without any prior causationassumptions, like in the case of the power law function, this can be furtheredin the future. To develop a comprehensive risk assessment tool, the suggestedprocedure can be expanded into a consequence assessment.126Chapter 7Consequence assessment of gaspipeline failure using anintegrated Bayesian networkand GIS7.1 BackgroundCorrosion is one of the main reasons for pipeline failure in the oil andgas industry. Because a pipeline failure can result in serious personal in-jury, monetary loss, and environmental damage, pipeline operators need tomake timely, and cost-effective decisions to prevent accidents in high con-sequence areas. The current study proposed integrating GIS and Bayesiannetwork to determine the consequence level of gas pipeline failure on thesociety and environment. To calculate the casualty of the pipe segment, themodel incorporates information such as pipe characteristics, failure mode,and population density. An event tree is used to represent all potential out-comes of a gas release based on the two most important variables that havea significant impact on accident evolution: the amount of time between agas leak and a potential ignition, and the possibility of an explosion dueto confinement from the environment. Finally the societal and environmen-tal consequence are estimated based on empirical equations, and subjectivejudgement, respectively. The spatial GIS capabilities combined with theBayesian network’s reasoning power creates a powerful tool for estimatingthe severity of pipe failure in a given area based on the information currentlyavailable.7.2 MethodologyThe current study introduces a spatial BBN technique as a tool for assess-ing the consequences of gas pipeline failure due to external pitting corrosion.1277.2. METHODOLOGYThe consequence can be measured in terms of social impact, environmentaldamage, and financial loss. The severity of these consequences varies basedon a number of factors, including the type of failure (such as rupture orleakage), the diameter of a pipe segment, the operating pressure, and thelocation of the event. Figure 7.1 displays the flowchart for the suggestedmodel. The main task of the model are calculating the pipe segment failureprobability, estimating the ignition probability, and constructing an eventtree (ET). The failure of a pipe segment can be due to burst or leakage.These failure modes are computed using the burst limit state function andleakage limit state function in the previous chapter. The failure probabilitydue to exterior pitting corrosion is defined as the greatest value of these twofailure types. After that, a pipe segment’s probability of igniting is calculateddepending on the failure type, pipe diameter, and operating pressure. In thesections that follow, this will be covered in more detail. The event tree isthen constructed using the explosion criterion and the pipe segment ignitionprobability. An event tree is used to illustrate the potential consequences ofa gas pipeline failure, and a BBN model is developed based on the event treeto estimate the effects of each output event on society and the environment.The next section also explains the details of the event tree and how it relatesto the casualty and environmental impact nodes. To determine the mostsensitive parameters, sensitivity analysis is then completed.Operating pressureFailure type (burst or leak)Leakage failure probabilityBurst failure probabilityIgnition probabilityExplosionPipe diameterCasualty estimationEnvironmental impactFigure 7.1: Flowchart for estimating the consequence of gas pipeline failuredue to external pitting corrosion.1287.2. METHODOLOGY7.2.1 Event treeA failure of gas pipeline poses a serious hazard to the lives and propertyof nearby residents. Because natural gas is flammable, explosive, and diffuse,various scenarios with differing outcomes have been used in the literature torepresent incidents involving natural gas pipelines (Sklavounos and Rigas,2006; Shahriar et al., 2012; Vairo et al., 2021). An event tree from Vairoet al. (2021) has been adopted in this study. The ET (represented in Figure7.2) was built to represent all potential outcomes of a gas release based onthe two most important variables that have a significant impact on accidentevolution: the amount of time between a gas leak and a potential ignition,and the possibility of an explosion due to confinement from the environment.The ET considers binary conditions, i.e., Yes or No, and propagates theevents until it reveals all potential output events.YesNoIgnition Early IgnitionYesNoExplosionYesNoScenarioJet/Pool fireExplosionFlash fireDispersionFailureNatural gas releaseFigure 7.2: Event tree analysis adapted to natural gas release.The findings of the previous chapter provided the failure probability ofa pipeline that results in the release of natural gas. Then, using empiricalmethods that will be presented in a subsequent section, the ignition proba-bility is computed. Here, the terms early ignition and delayed ignition aredifferentiated, with early ignition being defined as ignitions that occur upto 60 seconds following the starting of a gas release (Vairo et al., 2021). Amushroom-shaped gas cloud will emerge during this initial phase and willcontinue to grow and rise due to buoyancy and discharge momentum; if ig-1297.2. METHODOLOGYnited, this cloud would result in an explosion with significant overpressure(Sklavounos and Rigas, 2006; Vairo et al., 2021). In the ET, a fire-onlyevent results from an early ignition, while an explosion and fire result froma delayed ignition. The latter considers the likelihood of an explosion andsubsequent flash fire. If a pipe segment is not ignited, it causes dispersion.A detail discussion on outcome of events are summarized in Sklavounos andRigas (2006).When an ignited gas cloud is confined by buildings and other objects,it can cause a significant overpressure that can destroy structures. On theother hand, when there are no confining objects present, a vapour cloud isfree to expand, and its explosion creates very little overpressure. The buoyantcharacteristic of vapour, which inhibits the formation of a persistent vapourcloud at a ground level, further reduces the possibility of a severe flash fireresulting from delayed ignition. Consequently, the two main hazards are theheat effect of thermal radiation from a continuous jet fire and the collapse ofbuildings due to explosion. The thermal effect of the jet fire can be used whenestimating the likelihood of fatality from an accident because the explosion’shazard distance is smaller than that of the jet fire (Jo and Crowl, 2008).7.2.2 Social impactFigure 7.3 illustrates the BBN model developed for calculating casu-alty. The model’s components are ignition probability, early ignition, out-put events, explosion, and casualty estimation. Based on a pipe segment’sfailure type (such as burst, leaking, and none), diameter, and operating pres-sure, the probability of igniting is estimated. An earlier study found thateven in remote locations without any evident ignition sources, the burst oflarge-diameter, high-pressure transmission gas pipelines could result in ig-nited releases (Vairo et al., 2021). On the other hand, there is rare evidenceof a small-diameter pipeline operating at lower pressure that causes ignitedreleases. In order to determine the ignition probability for both burst andleakage failures, pipeline diameter and operating pressure were taken intoconsideration. Table 7.1 provides a summary of the ignition likelihood forpipeline burst and leaking.The probabilities of ignition and non-ignition are represented by the twostates (Yes and No) of the ignition node (Figure 7.3), respectively. Usingthe equations in Table 7.1, the CPT is estimated (Abdoul Nasser et al.,2021). Figure 7.4a and b display the contour plots of the probability ofigniting for burst and leakage, respectively. These contour plots reveal asignificant failure probability for large diameter and high operating pres-1307.2. METHODOLOGYIgnitionYesNo43.856.2Output EventJet fireExplosionFlash fireDispersionNone28.50.9214.422.833.4ExplosionYesNo6.0094.0Early ignitionYesNo28.571.5Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 307.147.147.147.147.147.147.147.147.147.147.147.147.147.1416 ± 8.1CasualtyVeryLowLowMediumHighVeryHigh64.38.703.902.3620.813.8 ± 23Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 150007.697.697.697.697.697.697.697.697.697.697.697.697.693660 ± 4600Diameter (mm)323.9355.6406.4457.2508558.8609.6660.4711.2762812.8863.6914.4965.210161066.81117.61168.41219.21320.81422.415241625.61727.21828.81930.420323.603.653.613.803.603.983.653.803.624.004.143.593.574.133.623.573.613.923.573.583.633.653.633.583.603.613.671030 ± 490Failure typeBurstLeakageNone33.433.333.4Leak failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 110.010.010.010.010.010.010.010.010.010.00.5 ± 0.29Burst failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 110.010.010.010.010.010.010.010.010.010.00.5 ± 0.29Figure 7.3: BBN model for calculating expected number of fatalities from agas pipeline failure.sure. The probability of early ignition is then calculated using data from thePHMSA database. The PHMSA database incident’s local date and time,as well as the incident’s ignition date and time, are utilized to estimate theearly ignition. According to the information, early ignition, or ignition thatoccurs in less than a minute, occurs in 65% of pipeline failures. The samedatabase also indicated that 6% of pipeline incidents ended in explosions.These details are incorporated into the BBN model and utilized to identifythe consequence type of a pipeline failure. The event tree described in theprevious sections is used to determine the output of the consequences.Based on the hazard radius, which can be computed as a function ofpipe diameter and operating pressure, failure type, and population density,the number of casualties are determined. Below are a few of the equations(Table 7.2) that were used to calculate the anticipated number of fatalitiesfrom being exposed to heat from a gas fire.The equation created by Nessim et al. (2009) is employed in the current1317.2. METHODOLOGYTable 7.1: Equations used to estimate the ignition probability.References FailuretypeEquations RemarksActon and Baldwin (2008)Rupture P(ign) = 0.0555 + 0.0137pd2; 0 ≤ pd2 ≤ 57 P(ign) = Probability of ig-nition, p = Pipeline oper-ating pressure (bar), d =Pipeline diameter (m)Acton and Baldwin (2008)Rupture P(ign) = 0.81; pd2 > 57 Similar as aboveActon and Baldwin (2008)Leakage P(ign) = 0.0555 + 0.0137(0.5pd2); 0 ≤ 0.5pd2 ≤ 57 Similar as aboveActon and Baldwin (2008)Leakage P(ign) = 0.81; 0.5pd2 > 57 Similar as above400 600 800 1000 1200 1400 1600 1800 2000Diameter (mm)51015202530Operating pressure (MPa)0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8(a)400 600 800 1000 1200 1400 1600 1800 2000Diameter (mm)51015202530Operating pressure (MPa)0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8(b)Figure 7.4: Contour plot of the ignition probability due to: (a) Burst and(b) Leakagestudy to calculate the expected number of fatalities. Nessim et al. (2009)made the assumption in their analysis that, as given in Equation 7.1, theprobability of ignition (pign) for burst is a linear function of pipe diameter.The model created by Stephens et al. (2002), in which the authors determinethe rate of gas leakage from pipeline as a function of pipe pressure and holesize, was also used to determine the hazard area (ah). Equation 7.2 is usedto calculate the proposed radial distance of the hazard area.pign = 0.0125 ∗Dia (7.1)r =√2348 ∗ P ∗Dia2Ith(7.2)1327.2. METHODOLOGYTable 7.2: Equations used to estimate expected number of fatalities resultingfrom exposure to heat emitted from a gas fire.References Equations RemarksNessim et al. (2009)N = piahρτ N = expected number of fatalities; pi = ignitionprobability; ah = hazard area; ρ = populationdensity τ = occupancy probabilityCunha (2016)Sc = V SL× ns× Pign;ns = δhπ3 (r21% + r2100% + r1%r100%)Sc = societal impact; VSL = value of statisticallife; ns = number of casualties; Pign = probabil-ity of ignition; δh = population densityJo and Ahn (2005)Ni =∫AiρPPidAi ρP = population density; Pi = lethality associ-ated with the accident scenario i; dAi = hazardareawhere P is pipe pressure (psi), Dia is pipe diameter (in), and Ith isthreshold heat intensity (Btu/hr/ft2). Therefore, using the radial distance,the area of the hazard can be determined as follows.ah = π2348 ∗ P ∗Dia2Ith(7.3)PipelineNo fatalitiesr100%r0%Possible fatalitiesCertain fatalitiesFigure 7.5: Illustration of societal impact integrationIn the literature, at least two hazard areas are considered to indicateregions with 100% and 0% fatality, respectively (Figure 7.5). As the distancefrom the source increases, the probability of fatality decreases until it is zeropercent. Lower and upper heat intensity thresholds are used to describe theheat intensity that people can take with little adverse effect and the heatintensity that almost certainly result in fatality. For indoor and outdoorexposure, the lower and upper heat intensity can also vary. The lower and1337.2. METHODOLOGYupper thresholds for outdoor exposure used in this study are 12.6 kw/m2and 31.6 kw/m2, respectively. The lower and upper thresholds for indoorexposure used in this study are 15.8 kw/m2 and 31.6 kw/m2, respectively.The probability of fatality between the 100% and 0% bounded area is finallyconsidered to be 50% for outdoor exposure and 25% for indoor exposure(Nessim et al., 2009). Equation 7.4 can now be used to calculate the expectednumber of fatalities as a weighted sum for the indoor and outdoor exposurescenarios.400 600 800 1000 1200 1400 1600 1800 2000Diameter (mm)51015202530Operating pressure (MPa)2 4 6 8 10 12 14 16 18 20 22400 600 800 1000 1200 1400 1600 1800 2000Diameter (mm)51015202530Operating pressure (MPa)10 20 30 40 50 60 70 80 90 100 110 120400 600 800 1000 1200 1400 1600 1800 2000Diameter (mm)51015202530Operating pressure (MPa)50 100 150 200 250 300 350 400 450 500 550400 600 800 1000 1200 1400 1600 1800 2000Diameter (mm)51015202530Operating pressure (MPa)500 1000 1500 2000 2500Figure 7.6: Expected number of fatalities due to rupture in (a) Location class1 (24 people/km2), (b) Location class 2 (132 people/km2) , (c) Location class3 (608 people/km2), (d) Location class 4 (3000 people/km2).N = pignρτ [Pi{ai−100h + 0.25(ai−0h − ai−100h )}+Po{ao−100h + 0.5(ao−0h − ao−100h )}](7.4)where Pi is proportions of time spent indoor, Po is proportions of time1347.2. METHODOLOGY400 600 800 1000 1200 1400 1600 1800 2000Diameter (mm)51015202530Operating pressure (MPa)0.5 1 1.5 2 2.5 3400 600 800 1000 1200 1400 1600 1800 2000Diameter (mm)51015202530Operating pressure (MPa)2 4 6 8 10 12 14 16 18400 600 800 1000 1200 1400 1600 1800 2000Diameter (mm)51015202530Operating pressure (MPa)10 20 30 40 50 60 70 80400 600 800 1000 1200 1400 1600 1800 2000Diameter (mm)51015202530Operating pressure (MPa)50 100 150 200 250 300 350 400Figure 7.7: Expected number of fatalities due to leakage in (a) Location class1 (24 people/km2), (b) Location class 2 (132 people/km2) , (c) Location class3 (608 people/km2), (d) Location class 4 (3000 people/km2).spent outdoor, the superscripts i and o denote indoor and outdoor, and100 and 0 denote the 100% and 0% fatality thresholds. For an occupancyprobability (τ) of 40%, with the time spent being indoor and outdoor are 90%and 10%, the number of fatalities given a burst (Nburst) can be calculatedby substituting Equation 7.1 and 7.3 into Equation 7.4 as follows:Nburst = 4.4× 10−8ρ ∗ P ∗Dia3 (7.5)where ρ is population density (PopDen) in people per hectare. By settingthe probability of igniting at a fixed value of 0.1, it is possible to calculate theexpected number of fatalities associated to leaking using a similar method.The expected number of fatalities associated with leakage is calculated insimilar approach by taking probability of ignition as a fixed value of 0.1.1357.2. METHODOLOGYFollowing is the derived equation.NL = 5.2× 10−7ρ ∗ P ∗ d2h (7.6)where dh is hole diameter (inches). All units are changed into metricunits for the analysis. The number of casualties for a specific pipe diameterand operating pressure is displayed on a contour plot. In accordance withASME specifications, four locations—Location 1= 24 people/km2, Location2= 132 people/km2, Location 3= 608 people/km2, and Location 4= 3000people/km2—were chosen to represent the average population density inthe four classes. The expected number of fatalities as a result of burstand leak failure types are shown in Figures 7.6 and 7.7, respectively. Ingeneral, leakage failure causes fewer fatalities than burst failure does for allclasses. Additionally, as population density increases, so does the amountof fatalities. Most transmission pipelines are installed in sparsely inhabitedareas. The fatalities from explosion and flash fire for both burst and leakare considered in this study to be 80% and 40% of the fatalities caused byjet fire as given by Equations 7.5 and 7.6, respectively.Discretization of the casualty BBN model is given in Table 7.3. In thisstudy, the word casualty is used interchangeably with fatalities. Based onthe study of Wu et al. (2017), the casualty node is discretized into five states:very low (0-2), low (2-10), medium (10-20), high (20-30), and very high (30-100).1367.2. METHODOLOGYTable7.3:DiscretizednodesoftheBBNcasualtymodel.ParametersSubcriteriaStatesCasualtyPopulationdensity(People/km2)VeryLow0≤PopDen<25Low25≤PopDen<50-High10000≤PopDen<12500VeryHigh12500≤PopDen<15000OP(MPa)VeryLow2≤OP<4Low4≤OP<6--High26≤OP<28VeryHigh28≤OP<30Diameter(mm)VeryLow323.9≤Diameter<355.6Low355.6≤Diameter<406.4--High1828.8≤Diameter<1930.4VeryHigh1930.4≤Diameter<2032EventtreeoutputJetfireJetfireExplosionExplosion Continuedonnextpage1377.2. METHODOLOGYTable7.3:DiscretizednodesoftheBBNcasualtymodel.(Continued)ParametersSubcriteriaStatesFlashfireFlashfireDispersionDispersionNoneNoneFailuretypeBurstBurstLeakageLeakageNoneNoneFailuretypeBurstprobability(BP)VeryLow0≤BP<0.1Low0.1≤BP<0.2--High0.8≤BP<0.9VeryHigh0.9≤BP<1Leakprobability(LP)VeryLow0≤LP<0.1Low0.1≤LP<0.2--High0.8≤LP<0.9VeryHigh0.9≤LP<1Continuedonnextpage1387.2. METHODOLOGYTable7.3:DiscretizednodesoftheBBNcasualtymodel.(Continued)ParametersSubcriteriaStatesIgnitionprobabilityOP(MPa)SimilarasaboveDiameter(mm)SimilarasaboveFailuretypeBurstBurstLeakageLeakageNoneNoneEventtreeoutputIgnitionprobabilityYesYesNoNoEarlyignitionYesYesNoNoFailuretypeSimilarasaboveExplosionYesYesNoNo1397.2. METHODOLOGY7.2.3 Environmental impactFigure 7.8 illustrates the BBN model developed for estimating environ-mental impact. Oil and gas pipelines may cross water bodies and travelthousands of kilometres in order to transport the energy product to the de-sired location. Pipeline operators always collect environmental data along apipeline route, including wildlife, vegetation, fish habitat, and others, whenthey intend to install new pipelines because a pipeline leak may have anadverse effect on the environment. In the current study, the environmentalimpacts are divided into two categories: water pollution from gas pipelinecrossings at bodies of water, and fires brought on by the ignition of gas thatmay have an influence on vegetation areas, animal species, and biodiversity.Diameter (mm)323.9355.6406.4457.2508558.8609.6660.4711.2762812.8863.6914.4965.210161066.81117.61168.41219.21320.81422.415241625.61727.21828.81930.420323.603.653.613.803.603.983.653.803.624.004.143.593.574.133.623.573.613.923.573.583.633.653.633.583.603.613.671030 ± 490IgnitionYesNo43.956.1Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 307.147.147.147.147.147.147.147.147.147.147.147.147.147.1416 ± 8.1ExplosionYesNo6.0094.0Output EventJet fireExplosionFlash fireDispersionNone28.50.9214.422.833.3Early ignitionYesNo28.571.5Failure typeBurstLeakageNone33.333.333.31 ± 0.82Burst failure probability Leak failure probabilityProtected and conserved areasYesNo50.050.00.5 ± 0.5Vegetation impactYesNo24.375.7Environmental impactLowMediumHigh59.421.119.4Water pollutionYesNo25.274.8Water bodyYesNo50.050.00.5 ± 0.5Figure 7.8: BBN model for environmental impact assessmentThe technique takes into account the likelihood that a failure will occuras a leak or burst, the likelihood that it would ignite, the outcome of theevent tree, and the severity of its environmental impact, similar to how itdoes for casualty model (Brito and de Almeida, 2009). In a prior study,1407.2. METHODOLOGYdifferent classes of land use (such as protected natural areas, tropical forests,secondary tropical forests, agricultural land, grassland, and inhabited areas)were scored according to their ecological relevance, and the ecological dam-age was estimated by multiplying the length of pipeline covered by theseland uses along pipeline routes. The selection of the route taken to build agas pipeline was backed by the developed approach by evaluating ecologicalimpact (Zúñiga-Gutiérrez et al., 2002).Discretization of the BBN model for environmental impact is given inTable 7.4. The vegetation impact node and water pollution node in theBBN model are used to qualitatively assess the environmental impact node.It is assumed that most water pollution results from a gas pipeline productreleases in a water crossing location. On the other hand, a gas pipeline leakthat catches fire could cause burning of green areas. The node for protectedand conserved areas contains places that can have an impact on ecology,such as wildlife refuges, ecological reserves, provincial pastures, natural areas,heritage rangelands, wilderness areas, special protection natural environmentparks, national wildlife areas, and migratory bird sanctuaries.1417.2. METHODOLOGYTable7.4:DiscretizednodesoftheBBNmodelforenvironmentalimpactassessment.ParametersSubcriteriaStatesEnvironmentalimpactVegetationimpactYesNoCasualtyWaterpollutionYesNoVegetationimpactProtectedandconservedareasYesNoWaterpollutionWaterbodyYesNoFailuretypeBurstprobability(BP)VeryLow0≤OPMn<0.1Low0.1≤OPMn<0.2--High0.8≤OPMn<0.9VeryHigh0.9≤OPMn<1Leakprobability(LP)VeryLow0≤OPMn<0.1Low0.1≤OPMn<0.2--High0.8≤OPMn<0.9Continuedonnextpage1427.2. METHODOLOGYTable7.4:DiscretizednodesoftheBBNmodelforenvironmentalimpactassessment.(Continued)ParametersSubcriteriaStatesVeryHigh0.9≤OPMn<1IgnitionprobabilityOperatingpressure(OP)(MPa)VeryLow2≤OP<4Low4≤OP<6--High26≤OP<28VeryHigh28≤OP<30Diameter(mm)VeryLow323.9≤Diameter<355.6Low355.6≤Diameter<406.4--High1828.8≤Diameter<1930.4VeryHigh1930.4≤Diameter<2032FailuretypeBurstBurstLeakageLeakageNoneNoneEventtreeoutputIgnitionprobabilityYesYesNoNoEarlyignitionYesYesContinuedonnextpage1437.2. METHODOLOGYTable7.4:DiscretizednodesoftheBBNmodelforenvironmentalimpactassessment.(Continued)ParametersSubcriteriaStatesNoNoFailuretypeSimilarasaboveExplosionYesYesNoNo1447.3. CASE STUDY7.3 Case studyThe gas pipeline in Alberta that was discussed in the previous work is ap-plied to the proposed methodology. Figure 6.5 displays the resulting pipelinenetwork for both natural gas and oil. Figure 6.6 also includes the diameterand operating pressure of the pipes in the network. Additionally, StatisticsCanada is used to acquire data such as population density needed for casu-alty estimation (Figure 7.9). Figure 7.9 depicts the population density persquare kilometre for the year 2016. Even though some data is missing, it isalso clear from the figure that, outside of the major cities, the majority ofthe province is sparsely inhabited.Population densityBand 1 (Gray)Alberta ShapefileFigure 7.9: Alberta’s population density per square kilometre in 2016.Information about protected places (such as natural areas, parks, ecologyreserves, and others) and water bodies (such as rivers and lakes) is gatheredfor the environmental impact assessment from the government of Canadawebsite. Figure 7.10 displays the protected and conserved areas for theprovince of Alberta and includes spatial data on both terrestrial and marineprotected areas. The inland waters of Alberta are depicted in Figure 7.11.1457.4. RESULT AND DISCUSSIONCanadian protected and conserved areasA - ParkEcological ReserveFlathead Watershed AreaHeritage RangelandMigratory Bird SanctuaryNational ParkNational Wildlife AreaNatural AreaNatural Environment ParkProvincial ParkProvincial PastureProvincial Recreation AreaSpecial Protection Natural Environment ParkWilderness AreaWilderness ParkWildland Provincial ParkWildlife RefugeAlbertaFigure 7.10: Alberta’s protected and conserved areas.7.4 Result and DiscussionA scenario analysis and sensitivity analysis are performed in order tovalidate the proposed model. In this study, the performance of the casualtymodel is evaluated using five different hypothetical scenarios. Table 7.5 liststhe parent node states and outputs for each of the five scenarios, includingfailure type, ignition, output events, and casualty. The pipeline diameter(762 mm) and operating pressure (6.9 MPa) are held constant in all fivescenarios.Scenario 1 represents class 3 location (500 people/km2) with burst andleak failure probability of 0.95 and 0.8, respectively. With the exception ofthe burst and leak failure probabilities, which are 0.8 and 0.95, respectively,scenario 2 is similar to scenario 1. The burst failures in scenarios 3 and 4 aredesigned to represent failures with average and low probabilities, respectively.The population density in Scenario 3 is 25 persons per square kilometre, andthe probabilities of burst and leak failure are 0.55 and 0.35, respectively.The population density in Scenario 4 is 130 persons per square kilometre,and the probabilities of burst and leak failure are 0.10 and 0.05, respectively.Scenario 5 represents class 4 location (2500 people/km2) with burst and leakfailure probability of 0.90 and 0.10, respectively.1467.4. RESULT AND DISCUSSIONAlberta Lakes and RiversAlbertaFigure 7.11: Alberta’s water body, including rivers and lakes.The greatest of the burst and leak probabilities is used by the failuretype node to represent the failure probability caused by external corrosion(Figure 7.5). Additionally, it is clear from scenarios 1 and 2 that burst failureproduces a larger likelihood of ignite than leak failure, even with the samefailure probability. Figure 7.4 illustrates this point, showing that even pipesegments with equal operating pressures and diameters have varied proba-bilities of ignition. Assuming a similar population density, the difference inignition probability leads to various output events and casualties. The effectsof burst failure probability and population density, which are both sensitiveparameters (Figure 7.12), on the model outputs are also shown in Scenarios3 and 4. A burst failure of a pipeline buried in a densely populated area cancause many casualties, as demonstrated in scenario 5.The results of sensitivity analysis for the BBN model is described in Fig-ure 7.12 for casualty, and environmental impact. The figures display thevariance reduction percentage for the parent node or independent input pa-1477.4. RESULT AND DISCUSSIONTable 7.5: Scenario analysis of the proposed BBN-based casualty estimationframework.Parent nodes Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Burst probability 0.95 0.80 0.55 0.10 0.90Leak probability 0.80 0.95 0.35 0.05 0.10Diameter 762 762 762 762 762Operating pressure 6.9 6.9 6.9 6.9 6.9Population density 500 500 25 130 2500Failure typeCasualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8041.82.0432.919.53.78 0 0 0 0 0 0 0 03.87 ± 2.8Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone37.820.4 036.94.94ExplosionYesNo 100 0IgnitionYesNo58.241.8Early ignitionYesNo37.862.2Failure typeBurstLeakageNone95.1 04.941.9 ± 0.43Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8073.726.3 0 0 0 0 0 0 0 0 0 0 01.53 ± 1.1Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone20.611.1 063.34.95ExplosionYesNo 100 0Ignit oYesNo31.68.3Early ignitionYesNo20.679.4Failure typeBurstLeakageNone 095.04.950.95 ± 0.22Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 80 100 0 0 0 0 0 0 0 0 0 0 0 01 ± 0.58Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 100 0 0 0 0 0 0 0 0 0 0 025Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone21.911.8 021.345.0ExplosionYesNo 100 0IgnitioYesNo33.766.3Early ignitionYesNo21.978.1Failure typeBurstLeakageNone55.0 045.01.1 ± 0.99Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Casualt0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 202 to 2525 to 303 to 404 to 5050 to 6060 to 8099.20.80 0 0 0 0 0 0 0 0 0 0 01.02 ± 0.6Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 100 0 0 0 0 0 0 0 0 0130Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone4.002.15 03.9089.9ExplosionYesNo 100 0Ignit oYesNo6.1693.Early ignitionYesNo4.0096.0Failure typeBurstLeakageNone10.1 089.90.201 ± 0.6Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8044.9 0 0 0 0 0 0 03.8625.911.014.3 023.4 ± 21Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 0 0 0 100 0 0 0 02500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone35.819.3 034.99.95ExplosionYesNo 100 0IgnitioYesNo55.144.9Early ignitionYesNo35.864.2Failure typeBurstLeakageNone90.1 09.951.8 ± 0.6Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030IgnitionCasualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8041.82.0432.919.53.78 0 0 0 0 0 0 0 03.87 ± 2.8Population density0 to 2525 to 5050 to 100100 to 200200 00400 800800 10001000 to 2500250 50005000 75007500 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone37.820.4 036.94.94ExplosionYesNo 100 0IgnitionYesNo58.241.8Early ignitionYesNo37.862.2Failure typeBurstLeakageNone95.1 04.941.9 ± 0.43Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8073.726.3 0 0 0 0 0 0 0 0 0 0 01.53 ± 1.1Population density0 to 2525 to 5050 to 100100 to 200200 t 00400 t 800800 t 10001000 to 2500250 t 50005000 t 75007500 t 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone20.611.1 063.34.95ExplosionYesNo 100 0IgnitionYesNo31.768.3Early ignitionYesNo2 .679.4Failure typeBurstLeakageNone 095.04.950.95 ± .22Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Cas alty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 80 100 0 0 0 0 0 0 0 0 0 0 0 01 ± 0.58Population density0 to 2525 to 5050 to 100100 to 200200 00400 800800 t 10001000 to 2500250 t 50005000 t 75007500 t 1000010000 to 1250012500 to 15000 0 100 0 0 0 0 0 0 0 0 0 0 02Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone21.911.8 021.345.0ExplosionYesNo 100 0IgnitionYesNo33.766.3Early ignitionYesNo21.978.1Failure typeBurstLeakageNone55.0 045.01.1 ± 0.99Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Cas al y0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8099.20.80 0 0 0 0 0 0 0 0 1.02 ± 0.6Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 100 0 0 0 0 0 0 0 0 013Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone4.002.15 03.9089.9ExplosionYesNo 100 0IgnitionYesNo6.1693.8Early ignitionYesNo4.0096.0Failure typeBurstLeakageNone10.1 089.90.201 ± 0.6Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8044.9 0 0 0 0 0 0 03.8625.911.014.3 03.4 ± 21Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 0 0 0 100 0 0 0 02500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone35.819.3 034.99.95ExplosionYesNo 100 0IgnitionYesNo55.144.9Early ignitionYesNo35.864.2Failure typeBurstLeakageNone90.1 09.951.8 ± .6Leak failure probabilityBurst failure probabilityDiame er ( n)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Output eventCasualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8041.82.0432.919.53.78 0 0 0 0 0 0 0 03.87 ± 2.8Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 100010 0 to 25002500 to 50005000 to 75007500 to 100010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 1 0 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone37.820.4 036.94.94ExplosionYesNo 100 0IgnitionYesNo58.241.8Early ignitionYesNo37.862.2Failure typeBurstLeakageNone95.1 04.941.9 ± 0.43Leak failure probabilityBurst failure probabilityDiameter (in)12.751461820222462830323463840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 803.726.3 0 0 0 0 0 0 0 0 0 0 01.53 ± 1.1Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 100010 0 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 01 0 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone20.611.1 063.34.95ExplosionYesNo 100 0IgnitionYesNo31.768.3Early ignitionYesNo20.679.4Failure typeBurstLeakageNone 095.04.950.95 ± 0.22Le k failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Casualty0 to 22 to 44 to 66 to 88 to 1212 to 616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 80 100 0 0 0 0 0 0 0 0 0 0 0 01 ± 0.58Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 100 0 0 0 0 0 0 0 0 0 0 025Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 1 0 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone21.911.8 021.345.0ExplosionYesNo 100 0IgnitionYesNo33.766.3Early ignitionYesNo21.978.1Failure typeBurstLeakageNone55.0 045.01.1 ± 0.99Le k failure probabilityBurst failure probabilityDiam te (in)12.751461820222462830323463840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Casualty0 to 22 to 44 to 66 to 88 to 1212 to 616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8099.20.80 0 0 0 0 0 0 0 0 0 0 01.02 ± 0.6Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 100 0 0 0 0 0 0 0 0 0130Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 200 to 2222 to 2424 to 2626 to 2828 to 3 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone4.002.15 03.9089.9ExplosionYesNo 100 0IgnitionYesNo6.1693.8Early ignitionYesNo4.0096.0Failure typeBurstLeakageNone10.1 089.90.201 ± 0.6Le k failure probabilityBurst failure probabilityDiameter (in)12.7514618202224628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8044.9 0 0 0 0 0 03.8625.911.014.3 023.4 ± 21Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 0 0 0 100 0 0 0 02500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone35.819.3 034.99.95ExplosionYesNo 100 0IgnitionYesNo55.144.9Early ignitionYesNo35.864.2Failure typeBurstLeakageNone90.1 09.951.8 ± 0.6Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030CasualtyIgnitionYesNo57.442.6Output EventJet fireExplosionFlash fireDispersi nNone37.31.2118.93 .46.18ExplosionYesNo6.0094.0Early ignitionYesNo37.362.7Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9CasualtyVeryLowLowMediumHighVeryHigh42.653.73.73 0 04.21 ± 3.7Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 125001250 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Failure typeBurstLeakageNone93.8 06.181.88 ± 0.48Leak failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 0 0 0 100 00.8Burst failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 0 0 0 0 1000.95IgnitionYesNo31.768.3Output EventJet fireExplosionFlash fireDispersionNone20.611.1 063.25.14ExplosionYesNo 100 0Early ignitionYesNo20.679.4Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9CasualtyVeryLowLowMediumHighVeryHigh68.331.7 0 0 02.58 ± 2.7Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 5005000 to 75007500 to 1000010000 to 1250012500 t 15000 0 0 0 0 0 100 0 0 0 0 0500Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Failure ty eBurstLeakageNone 094.95.140.949 ± 0.22Leak failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 0 0 0 0 1000.95Burst failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 0 0 0 100 00.8IgnitionYesNo33.666.4Output EventJet fireExplosionFlash fireDispersionNone21.811.8 021.345.2ExplosionYesNo 100 0Early ignitionYesNo21.878.2Operating pre sure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9CasualtyVeryLowLowMediumHighVeryHigh 100 0 0 0 01 ± 0.58Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 t 15000 0 100 0 0 0 0 0 0 0 0 025Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Failure typeBurstLeakageNone54.8 045.21.1 ± 1Leak failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 100 0 0 0 0 0 00.35Burst failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 100 0 0 0 00.55IgnitionYesNo6.3293.7Output EventJet fireExplosionFlash fireDispersionNone4.112.21 04.0089.7ExplosionYesNo 100 0Early ignitionYesNo4.1195.9Operating re sure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9CasualtyVeryLowLowMediumHighVeryHigh95.84.17 0 0 01.21 ± 1.2Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 100 0 0 0 0 0 0 0 0 0130Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Failure typeBurstLeakageNone10.3 089.70.207 ± 0.61Leak failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 100 0 0 0 0 0 0 0 0 00.05Burst failure probability to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 100 0 0 0 0 0 0 0 00.1IgnitionYesNo54.945.1Output EventJet fireExplosionFlash fireDispersionNone35.719.2 034.710.4ExplosionYesNo 100 0Early ignitionYesNo35.764.3Operating re sure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9CasualtyVeryLowLowMediumHighVeryHigh45.1 0 03.8451.029.5 ± 28Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 0 0 0 100 0 0 0 02500Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Failure typeBurstLeakageNone89.6 010.41.79 ± 0.61Leak failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 100 0 0 0 0 0 0 0 00.1Burst failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 0 0 0 0 1000.9Population densityDiameterBurst failure probabilityOperating pressureLeak failure probabilityExplosion0 2 4 6 8 10 12 14 16 18Percent (%)B BN no de s(a)Protected and conserved areaWater bodyBurst failure probabilityLeak failure probabilityDiameterOperating pressure0 1 2 3 4 5Percent (%)B BN no de s(b)Figure 7.12: Sensitive parameters of the BBN model for: (a) Casualty and(b) Environmental impactrameters. The parameters that have highest contributions in estimating thenumber of casualty are population density (17.30%), pipe diameter (7.75%),burst failure probability (4.03%), and operating pressure (2.02%). To a lesser1487.4. RESULT AND DISCUSSIONdegree, leak failure probability (0.24%) shows sensitivities as well. The pa-rameters that have highest contributions in predicting environmental impactare protected and conserved area (5.01%), water body (3.79%), burst failureprobability (1.6%), and leak failure probability (1.19%). To a lesser degree,diameter (0.03%), and operating pressure (0.01%) show sensitivities as well.Very low Low Medium High Very high0.00.20.40.60.81.0P ro ba bi l i ty 25%~75% Range within 1.5IQR Median Line Mean OutliersFigure 7.13: Casualty-probability for the five statesHere, a case study with the Alberta gas transmission pipeline is uti-lized to estimate the expected number of fatalities, and QGIS is used todisplay the results. The following two scenarios are taken into consideration:1) expected number of fatalities in the current year (2022) considering theavailable limited and incomplete information and 2) estimate the expectednumber of fatalities after ten years (2035) if no maintenance or rehabilita-tion is applied to the pipelines in the network. The outcome shows how thenetwork’s pipe segments’ consequence levels have changed. The informationon the probability of failure due to burst and leak is obtained from previouschapter. The analysis of each pipe in the network shows that the majorityof them have a very low probability of casualty (Figure 7.13). There are fewlow-state pipes in the gas network and extremely few medium-state pipelines,with some outliers in the medium state having up to 40% likelihood of ca-sualty. The outcome of the BBN casualty assessment method is shown in1497.4. RESULT AND DISCUSSIONAlbertaExpected casualty01234569111219Figure 7.14: BBN output for the casualty modelFigure 7.14 for the current year. In order to provide a clearer picture, theresults for the years 2022 and 2035 are compiled using a bar chart in Figure7.15. As observed in the bar graph, there are 2149 (97.55%), 51 (2.31%),and 3 (0.14%) pipe segments in 2022 that are classified as very low, low,and medium, respectively. Likewise, in 2035, there will be 2143 (97.28%),57 (2.59%), and 3 (0.14%) pipe segments classified as very low, low, andmedium, respectively. Each pipe segment’s class represents the state withit’s highest probability. The belief probability of the casualty states changedfor 2035, even though the majority of pipe segments remained in the verylow category. Between 2022 and 2035, almost 6 pipe segments moved froma very low to a low consequence category. This gradual decline is mainlycaused by two factors. The primary cause is that the vast majority of gastransmission pipelines are situated in an area with very little population. Nomatter how failure probability alter, this always results in low casualty rates.Population density is the most sensitive parameter in the model, thereforein the future, depending on the information available, it may be possible1507.4. RESULT AND DISCUSSIONto use population density from different years to produce a more sensitiveconclusion. Second, the failure probability of large diameter pipelines doesnot increase significantly in a short period of time. This can be seen in thenetwork’s average failure probability value shown in Figure 7.16.214951 3 0 02143573 0 0Very low Low Medium High Very high05001000150020002500Nu mb er of se gme nt sCasualty categories 2022 2035Figure 7.15: Pipe segment classification based on casualty classes.Moreover, the expected number of fatalities is calculated separately forthe burst and leak failures individually, without taking the maximum of thetwo probabilities at the beginning. This approach is put to the test usingScenario 2. The result is shown in Table 7.6. From the table, it is evidentthat the burst failure has a higher probability of casualties (an average of 4people) than the leakage failure probability (with an average of 3 people).Since it is clear from this that the casualty can be minimized if the maximumof both failure probabilities is taken into account at all times, it is advisedto compute the casualty separately for the two failure kinds.A simplified empirical equation can also be used to assess the impact areaof a gas pipeline failure. In the literature, Equation 7.3 is used to calculatethe radius of impact and assess the potential consequences of a natural gaspipeline failure.r = 0.69 ·Dia√MAOP (7.7)1517.4. RESULT AND DISCUSSION0.0 0.2 0.4 0.6 0.802004006008001000120014001600Co un tFailure probability Burst-2022 Leak-2022 PoF-2022 Burst-2035 Leak-2035 PoF-2035Normalmu sigmaBurst-2022 0.21973 0.27852Leak-2022 0.12528 0.17477PoF-2022 0.24883 0.27227Burst-2035 0.25561 0.29322Leak-2035 0.20037 0.20586PoF-2035 0.30294 0.28056Figure 7.16: Failure probability distribution for the pipeline segments in thesystem.CalgaryExpected casualty01234569111219Population densityBand 1 (Gray)(a)Expected casualty01234569111219EdmontonPopulation densityBand 1 (Gray)(b)Figure 7.17: BBN output for the casualty model, focusing on the city of: (a)Calgary and (b) Edmontonwhere r is the potential impact radius (ft), MAOP is pipeline segment’smaximum allowable operating pressure (psig), and Dia is the nominal pipe1527.4. RESULT AND DISCUSSIONTable 7.6: Analysis of the proposed BBN-based casualty estimation for burstand leak failure probabilities.Parent nodes Scenario 2 Scenario 2Burst probability 0.80 0Leak probability 0 0.95Diameter 30 30Operating pressure 6.9 6.9Explosion Yes YesPopulation density 500 500Failure typePopulation density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 150007.697.697.697.697.697.697.697.697.697.697.697.697.693660 ± 4600IgnitionYesNo56.443.6Output EventJet fireExplosionFlash fireDispersionNone36.71.1818.623.620.0ExplosionYesNo6.0094.0Early ignitionYesNo36.763.3Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8053.23.592.401.812.601.961.541.571.282.101.641.3724.922.1 ± 30Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 307.147.147.147.147.147.147.147.147.147.147.147.147.147.1416 ± 8.1Failure typeBurstLeakageNone80.0 020.0Leak failure probabilityBurst failure probabilityDiameter (in)323.9355.6406.4457.2508558.8609.6660.4711.2762812.8863.6914.4965.210161066.81117.61168.41219.21320.81422.415241625.61727.21828.81930.420323.603.653.613.803.603.983.653.803.624.004.143.593.574.133.623.573.613.923.573.583.633.653.633.583.603.613.671030 ± 490Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8073.726.3 0 0 0 0 0 0 0 0 0 0 01.53 ± 1.1Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone20.611.1 063.34.95ExplosionYesNo 100 0IgnitionYesNo31.768.3Early ignitionYesNo20.679.4Failure typeBurstLeakageNone 095.04.950.95 ± 0.22Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030IgnitionIgnitionYesNo49.450.6Output EventJet fireExplosionFlash fireDispersionNone32.117.3 031.319.3ExplosionYesNo 100 0Early ignitionYesNo32.167.9Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9CasualtyVeryLowLowMediumHighVeryHigh50.646.23.21 0 03.76 ± 3.6Population density0 to 2525 to 505 to 1001 0 to 2002 0 to 4004 0 to 8008 0 to 10001 00 to 25002500 to 50005 00 to 75007500 to 100001 000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Failure typeBurstLeakageNone80.7 019.3Leak failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 100 0 0 0 0 0 0 0 0 00Burst failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 0 0 0 100 00.8Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8073.726.3 0 0 0 0 0 0 0 0 0 0 01.53 ± 1.1Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone20.611.1 063.34.95ExplosionYesNo 100 0IgnitionYesNo31.768.3Early ignitionYesNo20.679.4Failure typeBurstLeakageNone 095.04.950.95 ± .22Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Output eventIgnitionYesNo49.450.6Output EventJet fireExplosionFlash fireDispersionNone32.117.3 031.319.3ExplosionYesNo 100 0Early ignitionYesNo32.167.9Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9CasualtyVeryLowLowMediumHighVeryHigh50.646.23.21 0 03.76 ± 3.6Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Failure typeBurstLeakageNone80.7 019.3Leak failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 100 0 0 0 0 0 0 0 0 00Burst failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 0 0 0 100 00.8Casualty0 to 22 to 44 to 66 to 88 to 1212 to 1616 to 2020 to 2525 to 3030 to 4040 to 5050 to 6060 to 8073.726.3 0 0 0 0 0 0 0 0 0 0 01.53 ± 1.1Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 0 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9Output EventJet fireExplosionFlash fireDispersionNone20.611.1 063.34.95ExplosionYesNo 100 0IgnitionYesNo31.768.3Early ignitionYesNo20.679.4Failure typeBurstLeakageNone 095.04.950.95 ± 0.22Leak failure probabilityBurst failure probabilityDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030CasualtyIgnitionYesNo49.450.6Output EventJet fireExplosionFlash fireDispersionNone32.117.3 031.319.3ExplosionYesNo 100 0Early ignitionYesNo32.167.9Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9CasualtyVeryLowLowMediumHighVeryHigh50.646.23.21 0 03.76 ± 3.6Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Failure typeBurstLeakageNone80.7 019.3Leak failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 100 0 0 0 0 0 0 0 0 00Burst failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 0 0 0 100 00.8IgnitionYesNo31.768.3Output EventJet fireExplosionFlash fireDispersionNone20.611.1 063.25.14ExplosionYesNo 100 0Early ignitionYesNo20.679.4Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9CasualtyVeryLowLowMediumHighVeryHigh68.331.7 0 0 02.58 ± 2.7Population density0 to 2525 to 5050 to 100100 to 200200 to 400400 to 800800 to 10001000 to 25002500 to 50005000 to 75007500 to 1000010000 to 1250012500 to 15000 0 0 0 0 0 100 0 0 0 0 0 0 0500Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Failure typeBurstLeakageNone 094.95.140.949 ± 0.22Leak failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 0 0 0 0 1000.95Burst failure probability0 to 0.10.1 to 0.20.2 to 0.30.3 to 0.40.4 to 0.50.5 to 0.60.6 to 0.70.7 to 0.80.8 to 0.90.9 to 1 0 0 0 0 0 0 0 0 100 00.8diameter (in.). A pipeline incident report of gas transmission is obtainedfrom PHMSA for 2010 - present, where it has information on the potentialimpact radius (PIR) for the location of the incident in feet. The PIR es-timated from Equation 7.7 and the actual PIR are c mpared as shown inFigure 7.18. The high coefficient of determination (R2 = 0.92) indicatesthat Equation 7.7 can estimate with PIR with high accuracy. The predictedPIR can be used in QGIS to indicate the possible impact area of each failingpipeline segment.A scenario analysis is performed to validate the BBN model for the envi-ronmental impact analysis. The performance of the model is evaluated usingfour different hypothetical scenarios. Table 7.7 lists the parent node states1537.4. RESULT AND DISCUSSION538 660 538.2 538 26 900602 660 601.7259 602 30 8451045 1000 1003.897 1045 42 1200296 660 296.2343 296 16 720332 660 331.7515 332 16 903539 660 439.4385 539 26 600526 660 525.7637 526 24 1008256 660 220.0051 256 12 706515 660 515.2873 515 26 825171 660 171.1437 171 8.625 827491 660 490.6904 491 2472 660 72.23611 72 4 685277 660 265.2413 277 12.75 909398 660 398.0529 398 16 1300560 660 487.9037 560 20 1250398 660 398.0529 398 16 1300314 660 333.8417 314 12.75 1440284 660 317.1984 284 12.75 1300805 660 805.2912 805 36 1051563 660 654.5915 563 30 1000640 660 633.2982 640 30 936524 660 523.6732 524 20 1440R² = 0.9202004006008001000120014001600180020000 500 1000 1500 2000Estimated PIR (ft)Observed PIR (ft)Figure 7.18: Comparison of the observed and estimated potential impactradius.and output for each of the four scenarios. The pipeline diameter (762 mm)and operating pressure (6.9 MPa) are held constant in all five scenarios. Inscenario 1, a pipe segment with failure probabilities for burst and leak of0.95 and 0.8, respectively, is situated close to an ecological area and spans abody of water. Scenario 2 is similar to scenario 1 except for the water bodythat the pipe does not cross. The failures in scenarios 3 and 4 that includebursting and leaking are intended to reflect failures with low likelihood. Incase 3, the pipe segment doesn’t cross any water bodies and is situated closeto an ecological area. In case 4, the pipe segment is not near an ecologicalarea and does not cross a body of water.The outcome of scenario 1 shows that the pipe segment has a significantenvironmental impact due to its high failure probability and proximity toboth protected areas and bodies of water. Due to the pipe’s lack of impactson any water bodies, scenario 2 exhibits relatively comparable beliefs forthe three states, and the environmental impact level reduced. However, thepipe segment in scenario 2 can have a medium or high environmental im-pact with an increase in pipe diameter and operating pressure. The outcomeof scenarios 3 and 4 points to a low likelihood of failure and, hence, a lowenvironmental impact. Additionally, the assumption of a low environmen-tal impact becomes more certain when pipe doesn’t traverse any ecological1547.5. SUMMARYTable 7.7: Scenario analysis of the proposed BBN-based environmental im-pact framework.Parent nodes Scenario 1 Scenario 2 Scenario 3 Scenario 4Burst probability 0.95 0.95 0.20 0.20Leak probability 0.80 0.80 0.10 0.10Diameter 762 762 762 762Operating pressure 6.9 6.9 6.9 6.9Protected and conserved area Yes Yes Yes NoWater body Yes No No NoEnvironmental impactDiameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9ExplosionYesNo6.0094.0Water pollutionYesNo70.929.1IgnitionYesNo58.241.8Water bodyYesNo 100 01Output EventJet fireExplosionFlash fireDispersionNone37.81.2219.236.94.94Protected and conserved areasYesNo 100 01Failure typeBurstLeakageNone95.1 04.941.9 ± 0.43Ecological impactYesNo64.335.7Environmental impactLowMediumHigh13.928.257.9Burst failure probability Leak failure probabilityEarly ignitionYesNo37.862.2Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9ExplosionYesNo6.0094.0Water pollutionYesNo4.7595.2IgnitionYesNo58.241.8Water bodyYesNo 0 1000Output EventJet fireExplosionFlash fireDispersionNone37.81.2219.236.94.94Protected and conserved areasYesNo 100 01Failure typeBurstLeakageNone95.1 04.941.9 ± 0.43Ecological impactYesNo64.335.7Environmental impactLowMediumHigh38.730.430.9Burst failure probability Leak failure probabilityEarly ignitionYesNo37.862.2Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9ExplosionYesNo6.0094.0Water pollutionYesNo1.0099.0IgnitionYesNo12.387.7Water bodyYesNo 0 1000Output EventJet fireExplosionFlash fireDispersionNone7.980.264.047.7779.9Protected and conserved areasYesNo 100 01Failure typeBurstLeakageNone20.1 079.90.401 ± 0.8Ecological impactYesNo13.686.4Environmental impactLowMediumHigh83.110.36.52Burst failure probability Leak failure probabilityEarly ignitionYesNo7.9892.0Diameter (in)12.751416182022242628303234363840424446485256606468727680 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030Operating pressure (MPa)2 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 2222 to 2424 to 2626 to 2828 to 30 0 0 100 0 0 0 0 0 0 0 0 0 0 06.9ExplosionYesNo6.0094.0Water pollutionYesNo1.0099.0IgnitionYesNo12.387.7Water bodyYesNo 0 1000Output EventJet fireExplosionFlash fireDispersionNone7.980.264.047.7779.9Protected and conserved areasYesNo 0 1000Failure typeBurstLeakageNone20.1 079.90.401 ± 0.8Ecological impactYesNo1.0099.0Environmental impactLowMediumHigh93.35.950.71Burst failure probability Leak failure probabilityEarly ignitionYesNo7.9892.0region or water body.In order to assess the environmental impact of exterior pitting corrosionfor the years 2022 and 2035, the Alberta gas transmission pipelines are usedas a case study. The analysis of each pipe in the network shows that themajority of hem have a low probability of having an adverse environmentalimpact (Figure 7.19). There are few medium-state pipes in the gas networkand extremely few high-state pipelines, with some outliers in the high statehaving up to 60% likelihood of high environmental impact. Figure 7.20displays the results of the BBN environmental impact assessment modelfor the current year, where each class represent the state with the highestprobability. Figure 7.21 compares the the state of segments using a bar chartfor th years 2022 and 2035. In 2022, there are 2179 (98.91%), 13 (0.59%),and 11 (0.50%) pipe segments that are categorized as low, medium, and high,respectively, according to the state with the highest probability, as seen inthe bar graph. Similarly, there will be 2166 (98.32%), 26 (1.18%), and 11(0.50%) pipe segments categorized as low, medium, and high, respectively, in2035. Almost 13 pipe segments went from being low to medium consequencecategory between 2022 and 2035.7.5 SummaryThis chapter provides a concept for an integrated BBN-GIS framework toevaluate the consequence of a gas pipeline failure caused by external pittingcorrosion. The framework begins by defining the variables that influence theconsequences analysis, such as failure type and its probability, diameter ofpipe section, operating pressure, and the location of an accident. Based onthe probability ignition, the amount of time between a gas leak and a prob-1557.5. SUMMARYLow Medium High0.00.20.40.60.81.0P ro ba bi l i ty Low Medium HighFigure 7.19: Environmental impact-probability for the three statesable ignition, and the potential for an explosion because of environmentalconfinement, the potential outcomes of a gas release are represented using anevent tree that was taken from a prior study. The previous chapter providesinformation on the failure probabilities of transmission gas pipelines in theAlberta network. Using data on failure probability, diameter, and operat-ing pressure, the ignition probability is calculated. The PHMSA databaseis used to estimate the probability of early ignition and explosion factors.The likelihood of an event tree’s outcome is calculated using each of theseinformation. The constructed BBN model then estimated the impact oncasualties and the environment using the outcome of the event tree.To assess the performance of the BBN models, sensitivity analysis andscenario analysis are conducted. The consequence level of pipe segmentsare also projected for the current year and 2035 in order to determine howthe degradation of the pipeline segment due to external corrosion impactsthe consequence measures. The proposed methodology is applied on the gastransmission pipeline in Alberta to show the efficacy and applicability ofthe proposed concept. Utilizing GIS, the pipe segments identified as havingsignificant consequence are shown. A pipeline could result in a significantnumber of casualties due to its high potential of burst failure and location1567.5. SUMMARYEnvironmental impactLowMediumHighAlbertaFigure 7.20: BBN output for the environmental impact modelin a densely populated region. Due to its high failure probability and prox-imity to both protected areas and water bodies, pipe segments have a majorimpact on the environment. The maximum of the burst and leak failureprobabilities is used for consequence assessment. The consequence level forpipe segments can be changed by evaluating the effects for the burst andleak failure probabilities separately and taking the maximum from the two.This is especially recommended when the likelihood of both a burst and aleak is quite high, but the likelihood of a burst is slightly less likely thanthat of a leak.The proposed approach can integrate and use all relevant open data toassess the impact of a gas pipeline, quantify uncertainty, and revise beliefswhen new information becomes available. For the analysis, the suggestedmethod can make use of both expert knowledge and historical data. To pre-vent bias resulting from expert subjectivity in the future, the model must beupdated using historical data. In order to have a comprehensive and trust-worthy analysis in the absence of this, it is advised to involve professionals1577.5. SUMMARY217913 11216626 11Low Medium High05001000150020002500Nu mb er of se gme nt sEnvironmental impact categories 2022 2035Figure 7.21: Pipe segment categorisation based on environmental impactstates.from various research backgrounds. In the future, historical data will need tobe used to verify the environmental impact model. Population density datais one of the input variables infromation that was collected for 2016, thus themodel must be run with updated data as it becomes available. The currentstudy excludes the third aspect of consequence, which is financial loss. Toassess the financial loss, additional aspects such as supply disruption, prop-erty and third-party damage, and others should be taken into account andconnected with GIS.158Chapter 8Conclusions andrecommendations8.1 Summary and conclusionsCorrosion in a pipeline can result in life-threatening situations, environ-mental damage, and financial losses. To improve their asset maintenancesystem, utilities need to adopt a proactive management strategy. The exter-nal corrosion of a pipeline varies widely from location to location dependingon the environment it is buried under. In order to help pipeline operators pre-vent pipeline failure due to external corrosion and reduce the consequencesof failure that could affect a high consequence area, decision support sys-tems must be developed. There aren’t many studies that concentrate on thenetwork level, and most proposed risk models for oil and gas pipelines are de-veloped and assessed at the pipe segment level. This dissertation suggesteda comprehensive decision support tool through the combination of BBN andGIS for assessing the risk associated with oil and gas pipelines in a networksubjected to external pitting corrosion.The dissertation begins with a bibliometric study to explain the scientificcontribution in this area. Numerous techniques, such as trend analysis, bib-liographic coupling, co-occurrence analysis, network analysis, and citationanalysis are employed in order to study the published papers related to thetopic. The published papers are collected from the WoS database using key-words linked to failure and consequence that were discovered through earlierresearch. The keyword analysis revealed that reliability analysis has beenstudied more frequently than consequence analysis. Also, research on nat-ural gas pipelines is more frequently conducted than on oil pipelines. Theco-occurence study indicates also that the failure analysis of an oil and gaspipeline is more completely studied than the analysis of its consequences. Ingeneral, it was challenging to find significant research papers that examinedthe consequences of pipeline failure using GIS to pinpoint regions with severeconsequences, which is crucial for pipeline operators to allocate resources for1598.1. SUMMARY AND CONCLUSIONSmonitoring.Before developing the risk assessment tool, publicly accessible corrosiondatabases were gathered from the literature and verified for completeness.Major problems with the corrosion database include missing values, out-liers, or an imbalance in the class distribution. Imputation methods anddata imbalance handling approaches are then used to handle these prevalentproblems. The missing values in a corrosion database are estimated usingimputation techniques. A dataset with a high missing rate causes decision-making to be biased. The National Bureau of Standards corrosion databaseis used in this research to examine eight imputation techniques. Mean, me-dian, linear regression, KNN, IRMI, AMELIA, IMPSEQ, and PCA are theeight imputation techniques. By using the datasets imputed by the eighttechniques, a NN is trained to assess the usefulness of the imputation tech-niques. The original corrosion dataset without the missing values was usedto train the NN model, and the results were compared with this baseline ac-curacy. KNN and IMPSEQ outperformed the other techniques by achievinga low error and high coefficient of determination.The majority of the measurements for corrosion pit depth in the Velázquezet al. (2009) corrosion database are for pipes buried in clay soil, with lessmeasurements available for the other soil categories. As a result, new sam-ples pertaining to the soil and pipe variables in the minority soil classes areproduced using class imbalance handling procedures. The methods includeborderline SMOTE, cGAN, and random oversampling. In order to developnew soil parameters depending on various soil types, this dissertation pre-sented a cGAN-based data augmenting method. Since the distribution ofthe generated data is similar to that of the original data, the technique isthought to be beneficial in developing a balanced corrosion database for assetmanagement. The usefulness of the cGAN method was assessed using a neu-ral network model. The neural network’s predicted performance increasedwhen it was trained using the balanced corrosion database.A bow-tie approach, which combines a fault tree with an event tree toexplain an event’s causes and consequences on a single platform, was usedas the risk assessment framework. In this study, an integrated BBN-GISmodel was developed to determine the probability of failure for oil and gaspipelines exposed to external pitting corrosion. The BBN model incorporatescorrosion, failure pressure, and limit state function. The advantages of usingempirical equations and a corrosion dataset to build a corrosion model werethoroughly discussed. The utility of equal interval, equal quantile, and mo-ment matching, three different discretization techniques, was also explored.Using examples, their impact on failure probability is assessed. According1608.2. ORIGINALITY AND CONTRIBUTIONSto the sensitivity analysis, the characteristics that relate to the pipe (suchas operating pressure, pipe wall thickness, diameter, UTS, coating type, andpipe age) or those that relate to the soil (such as pipe to soil potential, pH,and chloride content) are the most sensitive for the burst and leak failure.As a result, a parametric analysis is done to assess the performance of themodel by altering a few important model parameters. An Alberta case studyis used to illustrate the proposed BBN model.In this dissertation, an integrated BBN-GIS model is also presented forassessing the consequences of gas pipeline failure caused by external pittingcorrosion. It is possible to quantify the consequence in terms of social impact,environmental harm, and monetary loss. These consequences can range inseverity depending on a variety of factors, such as the type of failure (such asrupture or leakage), the diameter of a pipe segment, the operating pressure,and the accident’s location. The probable outcomes of a gas pipeline failureare illustrated using an event tree, and the impacts of each output event onsociety and the environment are estimated using a BBN model built on theevent tree. Based on the event tree’s outcomes and the area’s populationdensity, the estimated number of casualties are calculated. The most impor-tant factor that affects a fatality caused by a gas pipeline failure is found tobe population density. The environmental impact is also measured using thewater pollution and vegetation impact of a gas pipeline failure close to pro-tected and conserved areas. Finally, sensitivity analysis and scenario analysisare used to show the validity of the proposed framework. The proposed BBNmodel is demonstrated in case study in the province of Alberta.The proposed risk framework is developed by integrating a BBN and GIS.The reasoning power of the Bayesian network and the spatial capabilities ofthe GIS combine to form a powerful tool for guiding pipeline infrastructuremanagement decisions. Additionally, it assists pipeline operators in makingprompt and economical decisions to prevent accidents in locations with ahigh consequence.8.2 Originality and contributionsThis Ph.D. research is the first to develop a spatial decision supportframework with an integrated BBN and GIS for risk assessment of oil andgas pipelines exposed to external pitting corrosion. The framework aidspipeline operators in preventing failure and minimizing the effects of cor-rosion failure that may affect HCAs. The majority of earlier studies thatdeveloped corrosion pit depth prediction models utilizing corrosion datasets1618.2. ORIGINALITY AND CONTRIBUTIONSthat were made available to the public didn’t address the problems withthe corrosion database. This dissertation begins by addressing some of themost significant issues that can be found in a corrosion database, primar-ily missing values and an unbalanced class distribution. Then, to addressthese prevalent issues, imputation techniques and data imbalance handlingtechniques are employed. The machine learning-based techniques are provento be more reliable and advanced to construct a complete corrosion datasetin both the imputation techniques and data imbalance handling techniques.The outcomes of these studies improve the corrosion models’ ability to pre-dict corrosion pit depth. Pipeline infrastructures are geospatially distributedassets that are buried in a complex soil environment. Transmission pipes inthe oil and gas industry, for example, traverse across provincial or interna-tional borders. As a result, the risk assessment framework is essential forintegrating risk-related spatial information (such as pipe and soil related in-formation, population density, information on rivers and lakes, and ecologicalsensitive areas) in order to evaluate the likelihood of failure and the severityof the consequences of pipe segments in a network. The components of theBBN model are developed using empirical equations obtained from litera-ture and historical information on pipeline incidents. For instance, usingpreviously developed empirical equations, the ignition probability for burstand leak failure was calculated. The PHMSA historical accident records areused to determine the percentage of early and delayed ignitions. In order toprioritize their maintenance, rehabilitation, and replacement, the pipelinesclassified as high consequence pipes are indicated in GIS. The contributionsof this study are specifically outlined as follows:1. Develop imputation methods to handle incomplete and missing datain a corrosion pit measurement database.2. Develop a machine learning based cGAN technique to handle the classimbalance in a corrosion database.3. Implementing the BBN model requires discretizing continuous data,which is a critical step. The amount of information lost as a result ofthis procedure will always vary depending on the discretization tech-nique used and the number of intervals. The current study develops acorrosion model by comparing several discretization techniques.4. Develop an oil and gas pipeline failure assessment model using an in-tegrated BBN model and GIS. The proposed model made use of rasterdata that was related to pipes (such as diameter, thickness, ultimate1628.3. LIMITATIONS AND RECOMMENDATIONStensile strength, and operating pressure), as well as soil (such as watercontent, pH, soil type, and bulk density). The suggested method pro-vides the same advantages as segmentation in risk analysis by usingraster data to model the failure of a vast network of pipelines buriedbeneath varying environments.5. Develop an integrated BBN model and GIS for the purpose of assess-ing the consequence of a gas pipeline failure. The model uses spatialdata to assess the casualty and environmental impact of transmissionpipelines, including population density, water bodies, and ecologicallysensitive locations.8.3 Limitations and recommendationsThe following is a summary of the main limitations and recommendationsfor future research:1. The interaction of independent corrosion defects was not considered inthe current study. The likelihood that each pipeline segment will failis estimated based on the single maximum depth of a corrosion defectthat can form on a buried pipeline exposed to the soil environment fora number of years.2. The BBN corrosion model uses some soil factors that are time-dependent.Due to the scarcity of time-dependent soil data, they were, however,treated as a static variable and only a static BBN technique was used.Dynamic Bayesian networks should be used in future research to taketime-dependent soil factors into account.3. The cGAN model designed to handle the imbalance dataset can becompared with other GAN variations and architecture that reduceGAN models’ instability.4. The prediction accuracy of Bayesian networks is significantly increasedby discretization of continuous nodes. Future research should exten-sively explore the advantages of using the moment matching methodand other advanced discretization techniques.5. The oil pipeline is excluded from the consequence BBN model, whichconcentrates on the fatality and environmental impacts brought on bythe gas pipeline.1638.3. LIMITATIONS AND RECOMMENDATIONS6. The proposed BBN model can be expanded into a multi-hazard frame-work, where additional factors, such as stress corrosion cracking, inter-nal corrosion, and geohazards, can be added in order to determine thepipeline failure probability.7. When assessing the failure probability of the oil and gas pipelines inAlberta, some of the corrosion model’s most sensitive parameters (suchas the pipe to soil potential and chloride content) were absent. Theseparameters were estimated for the current study based on the type ofsoil a pipeline is buried under. In the future, however, efforts must bemade to gather data for such sensitive parameters before applying ina case study.8. In this study, the financial impact of a gas pipeline failure is not takeninto account. This can be added to the framework in the future toconsider supply interruptions, material loss, property and third partydamage into account.9. The impacts of climate change and extreme weather events vary fromregion to region. 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Import l i b r a r i e sl i b r a r y (mice )l i b r a r y ( bnst ruct )l i b r a r y ( naniar )l i b r a r y ( magr i t t r )l i b r a r y ( dplyr )l i b r a r y ( s imputat ion )l i b r a r y ( Amelia )l i b r a r y ( zoo )l i b r a r y ( rrcovNA )l i b r a r y ( imputeTS )l i b r a r y ( neura lne t )l i b r a r y (missMDA)l i b r a r y (VIM)l i b r a r y ( ca r e t )l i b r a r y (RANN)#4. V i s u a l i z a t i o n o f miss ing va lue s & c o r r e l a t i o n matrixgg_miss_var ( data )sum( i s . na ( data ) )188Appendix A. Example code for imputation techniquesres<− na . omit ( data )r e s <− data . matrix ( r e s )r e s <− cor ( r e s )r e s <− round ( res , 2)#5. Apply imputation techn iques#5.1 Meanda1 <− dataf o r ( i in 1 : nco l ( da1 ) ){da1 [ i s . na ( da1 [ , i ] ) , i ] <− mean( da1 [ , i ] , na . rm = TRUE)}#5.2 Medianda2 <− dataf o r ( i in 1 : nco l ( da2 ) ){da2 [ i s . na ( da2 [ , i ] ) , i ] <− median ( da2 [ , i ] , na . rm = TRUE)}#5.3 Linear Regres s ion#Dependent va r i ab l e : Var i ab l e s with miss ing v laues#Independent v a r i a b l e s : Var i ab l e s that have s i g n i f i c a n t . . .#c o r r e l a t i o n with the dependent va r i ab l eda3 <− datada3 <− impute_lm(da3 , Sand ~ S i l t+Clay+.. .+Moisture )da3 <− impute_lm(da3 , S i l t ~ Suspens ion+Re s i s t i v i t y+Sand )...da3 <− impute_lm(da3 , S p e c i f i c Gravity ~ Air pore +.. .+Sand )#5.4 KNNda4 <− datada4<−kNN(da4 , k=10)#5.5 IMPSEQ189Appendix A. Example code for imputation techniquesda5 <− datada5 <− impSeq ( data )#5.6 IRMIda6 <− datada6 <− i rmi ( data , i n i t . method = "median ")#5.7 AMELIAda7 <− datada7 <− amel ia ( x=da7 , m = 5 , incheck = TRUE)da7a <− da7$imputations$imp1da7b <− da7$imputations$imp2da7c <− da7$imputations$imp3da7d <− da7$imputations$imp4da7e <− da7$imputations$imp5da7 <− ( da7a+da7b+da7c+da7d+da7e )/5#5.8 PCAda8 <− datanb <− estim_ncpPCA(da8 , method . cv = "Kfold " , verbose = FALSE)# est imate the number o f components from incomplete datada8 <− imputePCA( data , ncp=nb$ncp )da8 <− da8$completeObs#6. Save the imputed da ta s e t swr i t e . csv ( da1 , " Imputation_mean . csv " , row . names = FALSE)wr i t e . csv ( da2 , " Imputation_median . csv " , row . names = FALSE)wr i t e . csv ( da3 , " Imputation_LR . csv " , row . names = FALSE)wr i t e . csv ( da4 , " Imputation_KNN . csv " , row . names = FALSE)wr i t e . csv ( da5 , " Imputation_IMPSEQ . csv " , row . names = FALSE)wr i t e . csv ( da6 , " Imputation_IRMI . csv " , row . names = FALSE)wr i t e . csv ( da7 , " Imputation_AMELIA . csv " , row . names = FALSE)wr i t e . csv ( da8 , " Imputation_PCA . csv " , row . names = FALSE)190
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Tesfamariam, Solomon
Development of a spatial decision support framework for risk assessment of oil and gas pipelines exposed to external pitting corrosion
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