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An Integrated Quantitative Risk Assessment Method for Urban Underground Utility Tunnels

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  • Wu, Jiansong
  • Bai, Yiping
  • Fang, Weipeng
  • Zhou, Rui
  • Reniers, Genserik
  • Khakzad, Nima

Abstract

With the rapid urbanization, urban underground utility tunnels have seen fast growth in China in the past few years. Urban utility tunnels can house various kinds of city ‘lifelines’ such as natural gas pipeline, heat pipeline, water supply system, sewer pipeline, electricity and telecommunication cables, which are of great significance to guarantee essential flows of energy, information and logistics for urban life. If a utility tunnel accident occurs, the consequences could be catastrophic. Risk assessment has been an important tool to examine the safety performance of industrial facilities and the effectiveness of safety measures. In this study, an integrated model based on dynamic hazard scenario identification (DHSI), Bayesian network (BN) modeling and risk analysis is proposed for risk assessment of urban utility tunnels. The worst-case scenario of urban utility tunnel accidents is identified by DHSI and modelled by BN. Meanwhile, risk analysis is conducted based on the results of BN considering casualties and economic losses. Finally, the integrated method is applied to evaluate the risk level of a real-world utility tunnel. The results indicate that the integrated quantitative risk assessment framework is an alternative and effective tool for safety assessment and land-use planning of urban utility tunnels.

Suggested Citation

  • Wu, Jiansong & Bai, Yiping & Fang, Weipeng & Zhou, Rui & Reniers, Genserik & Khakzad, Nima, 2021. "An Integrated Quantitative Risk Assessment Method for Urban Underground Utility Tunnels," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021003161
    DOI: 10.1016/j.ress.2021.107792
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    References listed on IDEAS

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    Cited by:

    1. Cai, Jitao & Wu, Jiansong & Yuan, Shuaiqi & Reniers, Genserik & Bai, Yiping, 2024. "Risk-based optimization of emergency response systems for accidental gas leakage in utility tunnels," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    2. Xue, Gang & Liu, Shifeng & Ren, Long & Gong, Daqing, 2024. "Risk assessment of utility tunnels through risk interaction-based deep learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Yong, Weixun & Zhang, Wengang & Nguyen, Hoang & Bui, Xuan-Nam & Choi, Yosoon & Nguyen-Thoi, Trung & Zhou, Jian & Tran, Trung Tin, 2022. "Analysis and prediction of diaphragm wall deflection induced by deep braced excavations using finite element method and artificial neural network optimized by metaheuristic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Hai, Nan & Gong, Daqing & Liu, Shifeng & Dai, Zixuan, 2022. "Dynamic coupling risk assessment model of utility tunnels based on multimethod fusion," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Zhang, Pei & Zhang, Zhen-Ji & Gong, Da-Qing, 2024. "An improved failure mode and effect analysis method for group decision-making in utility tunnels construction project risk evaluation," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    7. Sun, Bin & Li, Yan & Zhang, Yangyang & Guo, Tong, 2024. "Multi-source heterogeneous data fusion prediction technique for the utility tunnel fire detection," Reliability Engineering and System Safety, Elsevier, vol. 248(C).

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