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A probabilistic analysis method for evaluating the safety & resilience of urban gas pipeline network

Author

Listed:
  • Chen, Xing-lin
  • Huang, Zong-hou
  • Ge, Fan-liang
  • Lin, Wei-dong
  • Yang, Fu-qiang

Abstract

Urban gas pipeline networks (UGPN) provide important support for high-quality urbanization. Therefore, it is imperative to analyze and assess the potential failures of UGPN. A novel probabilistic analysis method is proposed for assessing the safety and resilience of UGPN. Firstly, Bow-Tie analysis is used to identify faults. Then, a four-dimensional resilience assessment network is developed. Bayesian network is utilized to model the relationship between the variables, while dynamic Bayesian network is used to consider the dynamic nature of the system. The results of the study show that the proposed model can accurately estimate faults, consequences, and influence paths. Furthermore, the resilience analysis shows that monitoring the objective conditions is crucial and that the initial failure probability of the UGPN decreases from 0.03767 % to 0.01435 % when connected to a resilience network, indicating that considering resilience can effectively improve the reliability and safety of the UGPN. Two application examples are presented in the paper to validate the functionality of the proposed model. The proposed model can be used to set the state of UGPN to predict the probability of occurrence of a specific event and its consequences and to simulate the improvement trend of UGPN based on the direction of focus of future work.

Suggested Citation

  • Chen, Xing-lin & Huang, Zong-hou & Ge, Fan-liang & Lin, Wei-dong & Yang, Fu-qiang, 2024. "A probabilistic analysis method for evaluating the safety & resilience of urban gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
  • Handle: RePEc:eee:reensy:v:248:y:2024:i:c:s0951832024002448
    DOI: 10.1016/j.ress.2024.110170
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    References listed on IDEAS

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    1. Li, Xin & Chen, Chao & Hong, Yi-du & Yang, Fu-qiang, 2023. "Exploring hazardous chemical explosion accidents with association rules and Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Chen, Yinuo & Xie, Shuyi & Tian, Zhigang, 2022. "Risk assessment of buried gas pipelines based on improved cloud-variable weight theory," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Liu, Wei & Song, Zhaoyang, 2020. "Review of studies on the resilience of urban critical infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Chen, Xing-lin & Yu, Long-xing & Lin, Wei-dong & Yang, Fu-qiang & Li, Yi-ping & Tao, Jing & Cheng, Shuo, 2023. "Urban resilience assessment from the multidimensional perspective using dynamic Bayesian network: A case study of Fujian Province, China," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    5. Xirong Bi & Jingxian Wu & Cheng Sun & Kun Ji, 2022. "Resilience-Based Repair Strategy for Gas Network System and Water Network System in Urban City," Sustainability, MDPI, vol. 14(6), pages 1-15, March.
    6. Yang, Yang & Li, Suzhen & Zhang, Pengcheng, 2022. "Data-driven accident consequence assessment on urban gas pipeline network based on machine learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Lanzano, Giovanni & Salzano, Ernesto & de Magistris, Filippo Santucci & Fabbrocino, Giovanni, 2013. "Seismic vulnerability of natural gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 73-80.
    8. Kammouh, Omar & Gardoni, Paolo & Cimellaro, Gian Paolo, 2020. "Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    9. Baikang Zhu & Xu Yang & Jun Wang & Chuanhui Shao & Fei Li & Bingyuan Hong & Debin Song & Jian Guo, 2022. "Third-Party Damage Model of a Natural Gas Pipeline Based on a Bayesian Network," Energies, MDPI, vol. 15(16), pages 1-12, August.
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