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Development of a non-Gaussian copula Bayesian network for safety assessment of metro tunnel maintenance

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  • Liu, Wenli
  • Shao, Yixiao
  • Li, Chen
  • Li, Chengqian
  • Jiang, Zehao

Abstract

The operation and maintenance of metro systems play a crucial role in urban development, with a focus on ensuring the serviceability and safety of metro tunnels. Accurate evaluation of the condition of these tunnel states requires investigating the complex interaction of multiple factors that impact their safety state. This study developed a hybrid model that integrates pair copula constructions (PCCs) and Bayesian networks (BN) to assess the safety state of metro tunnels, considering complex dependencies among these factors. First, key performance indicators (KPIs) were selected to assess tunnel safety, based on six failure modes. Then, an improved copula-based PC algorithm was employed to learn the non-Gaussian bayesian network model, which eliminated the normality assumption in the marginal densities of the KPIs. Finally, a combination of forward reasoning and GM(1,1) was utilized to predict the safety state of the metro tunnel in time series, facilitating the formulation of effective operation and maintenance plans. Furthermore, the proposed approach was applied to a real case study of the Wuhan metro system to demonstrate its effectiveness and applicability. The results highlighted the significant influence of some KPIs, such as convergence deformation, dislocation displacement, and stripping area, on metro tunnel safety. These KPIs emerged as key factors requiring focused attention in the operation and maintenance of metro tunnels.

Suggested Citation

  • Liu, Wenli & Shao, Yixiao & Li, Chen & Li, Chengqian & Jiang, Zehao, 2023. "Development of a non-Gaussian copula Bayesian network for safety assessment of metro tunnel maintenance," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:reensy:v:238:y:2023:i:c:s095183202300337x
    DOI: 10.1016/j.ress.2023.109423
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    Citations

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    Cited by:

    1. Hunte, Joshua L. & Neil, Martin & Fenton, Norman E., 2024. "A hybrid Bayesian network for medical device risk assessment and management," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Wang, Yifei & Xie, Mingjiang & Su, Chun, 2024. "Multi-objective maintenance strategy for corroded pipelines considering the correlation of different failure modes," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Wu, Pengcheng & Chen, Jun, 2024. "Data-driven zonotopic approximation for n-dimensional probabilistic geofencing," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Liu, Jintao & Chen, Keyi & Duan, Huayu & Li, Chenling, 2024. "A knowledge graph-based hazard prediction approach for preventing railway operational accidents," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    5. Teng, Da & Feng, Yun-Wen & Lu, Cheng & Liu, Jia-Qi & Chen, Jun-Yu, 2024. "Vectorial generative adversarial surrogate modeling reliability evaluation framework for engineering structural systems," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    6. Wen, Jiayi & Li, Xiaoxuan & Xue, Jingwei, 2024. "Feasibility evaluation of Copula theory for substation equipment with multiple nonlinear-related seismic response indexes," Reliability Engineering and System Safety, Elsevier, vol. 247(C).

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