Risk assessment of subway station fire by using a Bayesian network-based scenario evolution model
Abstract
Subway station fires frequently result in massive casualties, economic losses and even social panic due to the massive passenger flow, semiconfined space and limited conditions for escape and smoke emissions. The combination of different states of fire hazard factors increases the uncertainty and complexity of the evolution path of subway station fires and causes difficulty in assessing fire risk. Traditional methods cannot describe the development process of subway station fires, and thus, cannot assess fire risk under different fire scenarios. To realise scenario-based fire risk assessment, the elements that correspond to each scenario state during fire development in subway stations are identified in this study to explore the intrinsic driving force of fire evolution. Accordingly, a fire scenario evolution model of subway stations is constructed. Then, a Bayesian network is adopted to construct a scenario evolution probability calculation model for calculating the occurrence probability of each scenario state during subway station fire development and identifying critical scenario elements that promote fire evolution. Xi’an subway station system is used as a case to illustrate the application of Bayesian network-based scenario evolution model, providing a practical management tool for fire safety managers. The method adopted in this study enables managers to predict fire risk in each scenario and understand the evolution path of subway station fire, supporting the establishment of fire response strategies based on “scenario–response” planning.
Keyword : subway station, fire safety, scenario analysis, Bayesian network, deduction analysis, sensitivity analysis
How to Cite
Li, X., Yuan, J., Zhang, L., & Yang, D. (2024). Risk assessment of subway station fire by using a Bayesian network-based scenario evolution model. Journal of Civil Engineering and Management, 30(3), 279–294. https://doi.org/10.3846/jcem.2024.20846
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Fan, W., Liu, Y., Weng, W., & Shen, S. (2013). Introduction to the public safety science and technology. China Science Press (in Chinese).
Huang, L. D., Cai, G., Yuan, H. Y., & Chen, J.G. (2019). A hybrid approach for identifying the structure of a Bayesian network model. Expert Systems with Applications, 131, 308–320. https://doi.org/10.1016/j.eswa.2019.04.060
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Kahn, H., & Wiener, A. (1967). The Year 2000: A framework for speculation on the next thirty-three years. Daedalus, 96(3), 705–732.
Khakzad, N. (2018). Which fire to extinguish first? A risk‐informed approach to emergency response in oil terminals. Risk Analysis, 38(7), 1444–1454. https://doi.org/10.1111/risa.12946
Kim, H. Y., Rie, D. H., & Kim, J. Y. (2008). Fire risk assessment for subway station according to supply and exhaust conditions. Fire Science and Engineering, 22(5), 29–34.
Leu, S. S., & Chang, C. M. (2015). Bayesian-network-based fall risk evaluation of steel construction projects by fault tree transformation. Journal of Civil Engineering and Management, 21(3), 334–342. https://doi.org/10.3846/13923730.2014.890643
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Li, Q., Deng, Y., Liu, C., Zeng, Q., & Lu, Y. (2016). Modeling and analysis of subway fire emergency response: An empirical study. Safety Science, 84, 171–180. https://doi.org/10.1016/j.ssci.2015.12.003
Li, M., Jiang, Y., Wu, Z., & Fan, R. (2021). Real-time prediction of smoke spread affected by multiple factors in subway tunnel using CAERES-DNN mode. Fire Technology, 57(4), 2025–2059. https://doi.org/10.1007/s10694-021-01109-x
Lin, X. F., Song, S. X., Zhai, H. Y., Yuan, P. W., & Chen, M. L. (2020). Using catastrophe theory to analyze subway fire accidents. International Journal of System Assurance Engineering and Management, 11(1), 223–235. https://doi.org/10.1007/s13198-019-00942-2
Liu, Z. J., Ji, Z. S., & Lin, Y. (2010). Fire risk analysis in ship engine room based on Bayesian networks. Shipbuilding of China, 51(3), 199–205.
Long, Z., Liu, C., Yang, Y., Qiu, P., Tian, X., & Zhong, M. (2020). Full-scale experimental study on fire-induced smoke movement and control in an underground double-island subway station. Tunnelling and Underground Space Technology, 103, Article 103508. https://doi.org/10.1016/j.tust.2020.103508
Matellini, D. B., Wall, A. D., Jenkinson, I. D., Wang, J., & Pritchard, R. (2018). A three-part Bayesian network for modeling dwelling fires and their impact upon people and property. Risk Analysis, 38(10), 2087–2104. https://doi.org/10.1111/risa.13113
Musharraf, M., Smith, J., Khan, F., Veitch, B., & MacKinnon, S. (2016). Assessing offshore emergency evacuation behavior in a virtual environment using a Bayesian network approach. Reliability Engineering & System Safety, 152, 28–37. https://doi.org/10.1016/j.ress.2016.02.001
Nezhad, H. S., Zivdar, H., & Amirnia, A. (2015). Assessment of fire risk in passenger trains in tunnels using the FMEA model and Fuzzy Theory (A case study in the Zagros railway). Current World Environment, 10, Article 1158. https://doi.org/10.12944/CWE.10.Special-Issue1.137
Postma, T. J., & Liebl, F. (2005). How to improve scenario analysis as a strategic management tool?. Technological Forecasting and Social Change, 72(2), 161–173. https://doi.org/10.1016/S0040-1625(03)00152-5
Qiu, D., Qu, C., Xue, Y., Zhou, B., Li, X., Ma, X., & Cui, J. (2020). A comprehensive assessment method for safety risk of gas tunnel construction based on fuzzy Bayesian network. Polish Journal of Environmental Studies, 29(6), 4269–4289. https://doi.org/10.15244/pjoes/115979
Roh, J. S., Ryou, H. S., Park, W. H., & Jang, Y. J. (2009). CFD simulation and assessment of life safety in a subway train fire. Tunnelling and Underground Space Technology, 24(4), 447–453. https://doi.org/10.1016/j.tust.2008.12.003
Roshan, S. A., & Daneshvar, S. (2015). Fire risk assessment and its economic loss estimation in Tehran subway, applying Event Tree Analysis. Iranian Journal of Health, Safety and Environment, 2(1), 229–234.
Shafer, G. (1976). A mathematical theory of evidence. New Jersey: Princeton University Press. https://doi.org/10.1515/9780691214696
Shangguan, C., Yu, L., Liu, G., Song, Y., & Chen, J. (2021). Risk assessment of chronic obstructive pulmonary disease using a Bayesian network based on a provincial survey. Polish Archives of Internal Medicine-Polskie Archiwum Medycyny Wewnetrznej, 131(4), 345–355. https://doi.org/10.20452/pamw.15867
Tang, Y., Bi, W., Varga, L., Dolan, T., & Li, Q. (2022). An integrated framework for managing fire resilience of metro station system: Identification, assessment and optimization. International Journal of Disaster Risk Reduction, 77, Article 103037. https://doi.org/10.1016/j.ijdrr.2022.103037
Tian, C., & Yang, B. (2014). A DS evidence theory based fuzzy trust model in file-sharing P2P networks. Peer-to-Peer Networking and Applications, 7(4), 332–345. https://doi.org/10.1007/s12083-012-0153-7
Tsukahara, M., Koshiba, Y., & Ohtani, H. (2011). Effectiveness of downward evacuation in a large-scale subway fire using Fire Dynamics Simulator. Tunnelling and Underground Space Technology, 26(4), 573–581. https://doi.org/10.1016/j.tust.2011.02.002
Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling, 203(3–4), 312–318. https://doi.org/10.1016/j.ecolmodel.2006.11.033
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