International Construction Specialty Conference of the Canadian Society for Civil Engineering (ICSC) (5th : 2015)

Tracking indoor air quality of buildings using BIM Marzouk, Mohamed M.; Abdelbasset, Ibrahim G.; Al-Gahtani, Khalid Jun 30, 2015

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5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction    Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015    TRACKING INDOOR AIR QUALITY OF BUILDINGS USING BIM Mohamed M. Marzouk1,3, Ibrahim G. Abdelbasset1, Khalid Al-Gahtani2 1 Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt 2 Civil Engineering Department, King Saud University, Kingdom of Saudi Arabia  3 Abstract: Today, the demand of sustainable buildings is getting higher. The main purpose of buildings is to provide a comfortable living environment to their occupants, considering different aspects including thermal, visual and acoustic comfort as well as Indoor Air Quality. Life cycle assessments are related to many issues such as environmental concerns. Decreasing carbon foot print and energy consumption rates and increasing comfort level for the building users can help to achieve environmental improvements. This comfort level is related highly to Indoor Air Quality (IAQ). This research aims at improving environmental concerns using building information modeling. As-built BIM model is developed to act as a hub to allow transformation of information to an external database, extracted from the BIM Model in COBIE (Construction-Operations Building Information Exchange) format. The database is updated in a dynamic manner to reflect external environmental changes. The environmental changes are captured using sensors that can detect variations in temperature and humidity. Also, carbon emissions and energy consumption rates are reflected back on the model. A case study is presented to demonstrate the use of the proposed framework. 1 INTRODUCTION Green building concept has been adopted by the construction industry as a response for the global environmental challenges leading to successful results. Abbaszadeh et al. (2006) found that thermal comfort, air quality, furnishing, cleaning and maintenance achieved higher rates for satisfaction in LEED-certified green buildings compared to those non-green counter parts. On the other hand, clients are looking for the added benefits coming from applying the concept of sustainable buildings as they have to increase the capital cost invested to perform their projects Paul and Taylor (2008). Previous research concluded that the aspect of sustainable and green buildings will become the most common among people when they get sure of the benefits and financial gains achieved from their projects as a result of the occupants improved productivity (Zuo et al. 2014). This improved productivity is assigned to the comfortable and satisfying environment provided for their users. Interoperable Carbon Information Modeling (iCIM) project provides an online tool to facilitate carbon assessment of a building by informing designers regarding their decisions and impact of their decisions throughout the building life cycle (iCIM 2011). According to the norms of building automations, it was found that comfort is an important characteristic compared to the usual security and safety issues. It was found also that thermal comfort and air quality are very important and significant factors in deciding the building sustainability and the human comfort level (Singh et al. 2011, Kang and Park 2000).  According to the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE 2013) and International Organization for Standardization (ISO) 7730, thermal comfort is defined as a 066-1  “State of mind that expresses satisfaction with the thermal environment”. Paul and Taylor (2008) performed a comparison between a green university office building and two conventional universities office buildings all located in Australia. They conducted a survey using questionnaires to examine comfort and satisfaction of users. The questionnaire included temperature, humidity, lighting, aesthetics, acoustics, serenity and overall satisfaction. The green building was having a level of thermal comfort higher than the two conventional buildings. A sustainable building is a function of comfortable and healthy spaces, reducing the generated waste, reducing the used natural resources and lower usage of energy. Kang and Park (2000) developed an integrated comfort sense system for indoor climate, three measurement environmental parameters were used in the system; temperature, humidity and air flow. ISO 7730 considered other parameters such as clothing level, activity level and mean radiant temperature. ZigBee technology-based environment was developed to monitor the indoor and outdoor environmental measurements. The procedures of thermal comfort analysis were proposed in literature (Kumar et al. 2010, Kumar and Hancke 2014-a, Kumar and Hancke 2014-b). Smart systems of thermal comfort sensing can be utilized as a practical tool for monitoring thermal comfort in homes, automobiles, office buildings, etc. (Kumar and Hancke 2014-a). Air conditioning systems can only sense air temperature as they are having obstacles in placating inhabitants in the building environment. But the technology in this area developed new methodologies and designs to achieve the demand of the best possible smart air conditioning system as it can provide the required value of comfort and control in an optimized environment (Kumar et al. 2010, Kumar and Hancke 2014-b). Ventilation is considered one of the effective systems that can improve the internal Air quality (IAQ) (Paul et al. 2010, Liu and Liu 2005), whereas, HVAC is having different control ventilation strategies. One of these strategies is called dual-mode ventilation control method it can save about 8.3-28.3% in electricity usage compared to the conventional fixed-rate controlling strategies (Chao et al. 2004). Other factors such as lighting, internal motions and activities also can play important roles in achieving the goal of occupants comfort level and would be taken into consideration.  Building Information Modeling (BIM) is considered one of the shining technologies developed to increase the efficiency of construction industry. Its function was extended to help in the facility management process by using the As-built BIM model and it is used also in monitoring the facility behavior over its life time as a way to increase the level of control on the building even in operations or maintenance. These great advantages are results of the comprehensive information stored in the model during the construction process. The functional and physical characteristics of a facility can be modeled digitally using BIM (Marzouk et al. 2010). COBIE (Construction Operation Building Information Exchange) is one of the IFC (Industry Foundation Classes) formats was developed in the aim of facility management improvement. It exports all model data in a manner that helps the facility manager to apply a high level of control, understanding all the building components, improve data quality and cut costs at projects turnover. Marzouk and Abdelaty (2014) proposed an integration between subways BIM-based models and Facility management process using a semi-automated inspection system. The system was developed using Wireless Sensor Network (WSN) inside the subway for detecting temperature and humidity. This paper presents a study that was performed on a university building located in Riyadh City the capital of Saudi Arabia. The building is a multifunction building where it has offices, laboratories and a lecture hall. The aim of the study is to evaluate the building behavior over the year regarding the Indoor Air Quality issues (temperature and humidity) and improving the occupants comfort level with respect to energy consumption rates and carbon foot print. The result may play a role in providing sustainable building and a higher convince for owners in achieving valuable benefits. 2 PROPOSED MODEL The proposed model is developed to represent the idea of improving the IAQ in a certain facility building; the idea enhances the process of building design and management system with more data assessment for temperature and humidity taking into considerations their dynamic nature over the year due to the external environmental changes. The assessment results are used to improve the occupants comfort level inside the building with respect to the consumption and usage of energy and the carbon emissions produced. As-built BIM model is used to monitor the system behavior and weather the occupant’s comfort level is maintained or not. The model has to reflect any defects in the system behavior to the facility 066-2  manager. Tracking the temperature and humidity changes is performed through sensors located in all the building spaces. These sensors are provided by (Dantec Dynamics 2012). The proposed model links the as-built BIM model to the assessment results through an external data base that is updated frequently with the sensors data and updating the As-built BIM model back with these data. The data are analyzed in the external database to ensure that they provide the required comfort level and notify the facility manager with any defects through the BIM model if the comfort level is out of the comfort range. The facility manager is then allowed to locate the problem and apply the required response plan immediately. The COBIE is considered one of the special information exchange tools that is designed to help in facility management. The COBIE sheet is chosen to be an external database. As such, the can manage the process easily and respond to any defects immediately to maintain the occupants’ comfort level. The designated procedure of the proposed model is illustrated in Figure 1.                        Figure 1: Proposed model procedure 3 LOCATING SENSORS At the model first stage, the process of measuring temperature and humidity had to be optimized by tracking the temperature and humidity behavior over the year and their effect on the IAQ and the occupants comfort level. In response to this problem, a thermal analysis was performed on the building. This thermal simulation was developed on the As-built BIM model with the help of the Revit and Ecotect software. The procedures of the analysis were first performed on the Revit as all the model spaces were defined and the model was exported in the GBXML format as shown in Figure 2. Then the Ecotect role started by importing the GBXML file. The non-defined spaces were remodeled on the Ecotect and the as-built materials were defined. Then, the building functional data were defined. Each space function was defined according to its function (office, laboratory or lecture hall) and according to the function the settings were adjusted. The thermal properties of the building were selected; the HVAC system was a Fan Coil System working on the concept of Constant Air Valve (CAV) with an assumed efficiency of 95%. Then, the thermostat range for environmental temperature range for comfort was defined, and then the lighting settings were set for each specified space. Then, the operation schedule was defined according to the hours of operations of each zone, these data were assumed according to the University working days. Sensors measurements Energy Usage Alert for User As-Built Information COBIE 066-3   Figure 2: Defining spaces using Revit and exported GBXML file format  After defining the space properties, the building was located on the map in its accurate location using Google Earth as shown in Figure 3. Then, the Riyadh city weather file was attached to the model and the solar path was defined according to the building orientation. Figure 4 shows monthly charts for the general effect of defined data on the building. After running the simulation, the critical spaces were identified due to their affection with external environmental changes through the year so that the analysis could be completed in an efficient way. The critical spaces were not all highest points of temperature and humidity in the building, but also lowest temperatures and humidity zones were critical so that it is possible to predict other building spaces data using interpolations and averages after receiving the actual data measured from the sensors located in the building.  Figure 3: Locating building location using Google Earth Space 5 066-4   Figure 4 Defining Solar path and model orientation 4 LOCATING SENSORS This section presents the experiment procedures and the methodology of analyzing the experiment outputs. The sensors were located in the building according to the performed thermal analysis. Ten spaces were chosen to monitor the internal air quality with respect to temperature and humidity. Three temperature probes and humidity probes were used in each space at the same time to improve the measurement accuracy Table 1. The experiment was performed in 23rd of November 2014 at 14:00 with a switched off Air Conditioning. Sample of the results are shown in Table 1. The sensors’ measurements were compared to the results of the thermal analysis that was conducted using Ecotect software. The thermal analysis was performed by applying the same experiment conditions. The thermal analysis results were approximately similar to the sensors measurements and that increased our trust in the performed thermal analysis even in defining the critical zones inside the building or in further capabilities in monitoring the indoor air quality. One of the critical spaces (named Space 5) was considered for the analysis (see Figure 2). A sample of the thermal analysis that shows inside and outside temperatures is depicted in Figure 5, taking into consideration conditions that are listed in Table 2.  Table 1: Humidity and Temperature probes measurements  Probe  X [m] Y [m] Z [m] Relative Humidity [%] Operative Temperature [oC] Hum. & Temp. 1 3.00 1.74 5.60 30.92 31.79 Hum. & Temp. 2 3.00 1.11 5.60 30.66 31.73 Hum. & Temp. 3 3.00 0.68 5.60 30.49 31.56 066-5   Table 3: Simulation results of the alternatives Simulation Original Case (RUP-BIM Model.xml) Simulation Experiment 1 (VAV Alternative) Simulation Experiment 2 (VAV and Lighting sensors) Annual Elec. Cost (SAR) 138,807 134,804 133,254 Annual Fuel Cost (SAR) 691 691 691 Elec. Demand (KW) 328.4 320.1 316.7 Annual Elec Use (KWh) 1,478,242 1,435,616 1,419,110 Annual Fuel Use (MJ) 93,062 109,767 93,062 Energy Use Intensity (MJ/m2/year) 767.7 748.3 737.5 Carbon Emissions (MG) 537 522.5 515.7   Figure 6: Annual energy consumption  066-7   Figure 7: Effect of different factors on energy usage 6 UTILIZING COMFORT LEVEL COBIE (Construction Operation Building Information Exchange) is considered one of the tools that can help in the process of facility management. It is used to provide two main advantages; the first is that the extracted COBIE sheet enables a full monitoring on the building objects and a system regarding their operations and maintenance, and the second is linking all the previous stages together to track and improve IAQ (see Figure 8). All measurements coming from sensors are imported into the COBIE sheet in a new tab in tabular form matching the COBIE format as the new tab could be linked with the Sheet using the unique element identifier. Also, the Energy analysis data are imported to the COBIE using the same procedures. The method of importing sensors measurements and Energy analysis to the COBIE is performed with the aid of Visual Basic for Applications programming language (VBA). The imported measurements are compared to the standard comfort range for temperature and humidity as stated by (ASHRAE, 2013); Temperature (22-25oC) and Humidity (31-41%) and if the sensors measurements were out of the comfort range the COBIE will reflect this back on the BIM model and give alert to the user with these out of range measurements so that the user can fix the problem immediately. Alerts would be a notification email to the responsible person. Another COBIE function is its ability to give the user alerts in case of any system expected shortage due to the end of its service life. These problems may be concerned with the HVAC system, lighting system or any system that could affect the users’ comfort level regarding the IAQ.  066-8   Figure 8: Sample of COBIE sheet 7 CONCLUSION This paper presented a model for tracking IAQ using BIM. The model is performed with the aid of different tools such as Revit, Bentley AECOsim, Ecotect, Green Building Studio and COBIE to increase its functionality. COBIE is extracted from the As-Built BIM model to act as a database for different measurements conducted through the building service time such as temperature, humidity, energy usage and carbon emissions. This database is frequently analyzed to make sure that the measurements are fulfilling the occupants comfort level and reports any negative incidents to the BIM model giving alerts to the model user. The paper also proposed some optimized alternatives to the existing design such as using the Variable Air Valve HVAC system instead of Fan Coil Unit and using lighting occupancy sensors instead of manual switches. These alternatives maintained the required comfort level with decreasing energy consumption and carbon emissions. 8 ACKNOWLEDGEMENT This project was supported by the NSTIP strategic technologies program number (11-BUI2090-02) in Kingdom of Saudi Arabia. References Abbaszadeh S., Zagreus L, Lehrer D, Huezenga C. 2006. Occupant Satisfaction with Indoor Environmental Quality in Green Buildings. Proceedings of Healthy Buildings, Lisbon, Portugal, III: 365-370. ASHRAE, 2013. “ASHRAE Hand Book”. 1st ed. Atlanta: W. Stephen Comstock, Publisher. Chao, C.Y.H. and Hu, J.S. 2004. Development of a dual-mode demand control ventilation strategy for indoor air quality control and energy saving, Building and Environment, 39(4): 385–397. Dantec Dynamics. 2014. “Comfort Sense Installation and User Guide”, January 2014. Retrieved from iCIM 2014. Interoperable Carbon Information Modeling. July 2014Retrieved from: 066-9  Kang, J. and Park, S. 2000. Integrated comfort sensing system on indoor climate. Sensors and Actuators A: Physical, 82(1–3): 302-307. Kumar, A. and Hancke, G.P. 2014-a. Energy Efficient Environment Monitoring System Based on the IEEE 802.15.4 Standard For Low Cost Requirements. Sensors Journal, IEEE, 14(8):2557–2566.  Kumar, A. and Hancke, G.P. 2014-b. An Energy-Efficient Smart Comfort Sensing System Based on the IEEE 1451 Standard for Green Buildings. Sensors Journal, IEEE, 14(12): 4245 – 4252. Kumar, A., Singh, I.P. and Sud, S.K. 2010. An Approach towards Development of Pmv Based Thermal Comfort Smart Sensor. International Journal on Smart Sensing and Intelligent Systems, 3(4): 621–642. Liu, J. and Liu, G. 2005. Some indoor air quality problems and measures to control them in China. Indoor and Built Environment, 14(1): 75–81. Marzouk, M. and Abdelaty, A. 2014. BIM-based Framework for Managing Performance of Subway Stations. Automation in Construction, 41(1): 70-77. Marzouk, M., Hisham, M., Ismail, S., Youssef, M. and Seif, O. 2010. On the Use Of Building Information Modeling in Infrastructure Bridges. Proceedings of 27th International Conference – Applications of IT in the AEC Industry (CIBW78), Cairo, Egypt, 136: 1–10. Paul W.L. and Taylor P.A. 2008. A Comparison of occupant comfort and satisfaction between a green building and a conventional building. Building and Environment, 43(11) 1858–1870. Paul, T., Sree, D. and Aglan, H. 2010. Effect of Mechanically Induced Ventilation on the Indoor Air Quality of Building Envelopes. Energy and Buildings, 42(3): 326–332. Singh, M.K., Mahapatra, S. and Atreya, S.K. 2011. Adaptive Thermal Comfort Model for Different Climatic Zones of North-East India. Applied Energy, 88(7): 2420–2428. Zuo, J., and Zhao Z-Y. 2014. Green Building Research Current Status and Future Agenda: A Review. Renewable and Sustainable Energy Reviews, 30: 271–281.   066-10  


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