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Modeling risks in dependent systems: A Copula-Bayesian approach

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  • Pan, Yue
  • Ou, Shenwei
  • Zhang, Limao
  • Zhang, Wenjing
  • Wu, Xianguo
  • Li, Heng

Abstract

This paper develops a hybrid Copula–Bayesian-based approach to model the structural health of an operational metro tunnel in a dependent system, where risk factors interact with each other. Aiming to rectify drawbacks of discrete Bayesian networks, the Gaussian copula and different marginal distributions are involved to build up complicated interactions among possible risk factors and structural health state more precisely and rationally. To achieve systematic and real-time decision support for safety insurance during the entire life cycle of risk-prone systems, model analytics is conducted to perform correlation analysis, forward reasoning, and backward reasoning. For demonstrating the effectiveness of the method, it is applied to a realistic cross-river tunnel, the Wuhan Yangtze Metro Tunnel in China, where all the concerning risk factors are water-related rooting in water leakage, poor construction quality, exterior load, and others. Due to the availability of vault displacement by sensors, it can herein represent the structural health to explore the impacts of risk factors. Larger vault displacement implies higher risk in the overall safety condition of the tunnel structure. This research contributes to (a) the state of knowledge by integrating Bayesian networks with copula, contributing to a more robust risk assessment by accurately modeling the complex dependence structure of risk factors; (b) the state of practice by providing guidelines of the whole-life-cycle safety control for complex systems under uncertainty and randomness, which not only prevent structural failure in advance but also control risk after accident occurrence.

Suggested Citation

  • Pan, Yue & Ou, Shenwei & Zhang, Limao & Zhang, Wenjing & Wu, Xianguo & Li, Heng, 2019. "Modeling risks in dependent systems: A Copula-Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 416-431.
    • Handle: RePEc:eee:reensy:v:188:y:2019:i:c:p:416-431
    DOI: 10.1016/j.ress.2019.03.048
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    References listed on IDEAS

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    9. Mei Liu & Boning Li & Hongjun Cui & Pin-Chao Liao & Yuecheng Huang, 2022. "Research Paradigm of Network Approaches in Construction Safety and Occupational Health," IJERPH, MDPI, vol. 19(19), pages 1-22, September.
    10. Li, Yaohan & Dong, You & Guo, Hongyuan, 2023. "Copula-based multivariate renewal model for life-cycle analysis of civil infrastructure considering multiple dependent deterioration processes," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    11. Chen, Tianyi & Wong, Yiik Diew & Shi, Xiupeng & Wang, Xueqin, 2022. "Optimized structure learning of Bayesian Network for investigating causation of vehicles’ on-road crashes," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    12. Ding, Feng & Wang, Yihua & Ma, Guoliang & Zhang, Xinrui, 2021. "Correlation reliability assessment of artillery chassis transmission system based on CBN model," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    13. Liu, Xiang-dong & Pan, Fei & Cai, Wen-li & Peng, Rui, 2020. "Correlation and risk measurement modeling: A Markov-switching mixed Clayton copula approach," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    14. Eryilmaz, Serkan & Ozkut, Murat, 2020. "Optimization problems for a parallel system with multiple types of dependent components," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    15. Wenjun Zhang & Xiangkun Meng & Xue Yang & Hongguang Lyu & Xiang-Yu Zhou & Qingwu Wang, 2022. "A Practical Risk-Based Model for Early Warning of Seafarer Errors Using Integrated Bayesian Network and SPAR-H," IJERPH, MDPI, vol. 19(16), pages 1-14, August.
    16. Wang, Ying & Zhang, Limao, 2021. "Simulation-based optimization for modeling and mitigating tunnel-induced damages," Reliability Engineering and System Safety, Elsevier, vol. 205(C).

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