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An Integrated Model for Risk Assessment of Urban Road Collapse Based on China Accident Data

Author

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  • Zewei Zhang

    (Research Institute of Emergency Science, Chinese Institute of Coal Science (CICS), Beijing 100013, China
    China Coal Technology & Engineering Group (CCTEG), Beijing 100013, China)

  • Qingjie Qi

    (Research Institute of Emergency Science, Chinese Institute of Coal Science (CICS), Beijing 100013, China
    China Coal Technology & Engineering Group (CCTEG), Beijing 100013, China)

  • Ye Cheng

    (Research Institute of Emergency Science, Chinese Institute of Coal Science (CICS), Beijing 100013, China
    China Coal Technology & Engineering Group (CCTEG), Beijing 100013, China)

  • Dawei Cui

    (Research Institute of Emergency Science, Chinese Institute of Coal Science (CICS), Beijing 100013, China
    China Coal Technology & Engineering Group (CCTEG), Beijing 100013, China)

  • Jinghu Yang

    (Research Institute of Emergency Science, Chinese Institute of Coal Science (CICS), Beijing 100013, China
    China Coal Technology & Engineering Group (CCTEG), Beijing 100013, China)

Abstract

With the deepening development and utilization of urban underground space, the risk of urban road collapse is becoming increasingly prominent, which is a serious threat to the safety of life and property. Therefore, the risk assessment of urban road collapse has vital significance for the safety management of cities. The main idea is to predict ongoing accidents by analyzing historical accident cases in depth. This paper explores the combination of Interpretative Structural Modeling (ISM) and Bayesian Networks (BNs) to construct a risk assessment model of road collapse. First, the main risk factors of road collapse and their coupling relationships are identified, which is used to increase the low reliability of complex systems. Then, the risk factors of road collapse are logically divided by ISM to obtain the BN hierarchy. Finally, the BN node probabilities are evaluated by the Expectation–Maximization (EM) algorithm using the collected 92 real road collapse accident cases. The model can be used to quantify the coupling strength and influence degree of each risk factor on the occurrence of road collapse accidents, which in turn can predict the probability of road collapse accidents in a given scenario. This study can provide a theoretical basis for urban safety management and reduce the risk of road collapse and potential loss of life and property, which is important for the sustainable development of societies.

Suggested Citation

  • Zewei Zhang & Qingjie Qi & Ye Cheng & Dawei Cui & Jinghu Yang, 2024. "An Integrated Model for Risk Assessment of Urban Road Collapse Based on China Accident Data," Sustainability, MDPI, vol. 16(5), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2055-:d:1349696
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    References listed on IDEAS

    as
    1. Ioannis Kotaridis & Maria Lazaridou, 2022. "Integration of convolutional neural networks for flood risk mapping in Tuscany, Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 3409-3424, December.
    2. Xu-Wei Wang & Ye-Shuang Xu, 2022. "Investigation on the phenomena and influence factors of urban ground collapse in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 1-33, August.
    3. Aihua Wei & Duo Li & Yahong Zhou & Qinghai Deng & Liangdong Yan, 2021. "A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 405-430, January.
    4. Predrag Mimović & Jelena Stanković & Vesna Janković Milić, 2015. "Decision-making under uncertainty – the integrated approach of the AHP and Bayesian analysis," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 28(1), pages 868-878, January.
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