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An integrated model for evaluating the leakage risk of urban gas pipe: a case study based on Chinese real accident data

Author

Listed:
  • Qing Deng

    (University of Science and Technology Beijing)

  • Kuo Wang

    (Beihang University)

  • Jiahao Wu

    (China Coal Research Institute)

  • Feng Yu

    (Shanghai Jiao Tong University)

  • Huiling Jiang

    (University of Science and Technology Beijing)

  • Lida Huang

    (Tsinghua University)

Abstract

Urban gas pipe network (GPN) is an important infrastructure to guarantee residents’ daily life. However, the risk of GPN has become increasingly prominent. Leakage is one of the biggest issues, which is easy to cause fire, explosion, poisoning, and so on. Therefore, the risk assessment of leakage is significant for the safety management of urban GPN. The main idea is to analyze the history accidents and predict the accidents that are happening. This paper explores to construct an integrated assessment model through Bayesian network (BN), Interpretive structural model (ISM), and expert evaluation method. First, the main risk factors of leakage and their coupling relationship are determined to increase the understanding of the complex system. Then, ISM is used to divide the logical network of factors to determine the hierarchical structure of BN. Finally, node probability is evaluated by Expectation–Maximization algorithm with the data collection of 89 real accidents. The model can be used to quantify the coupling strength and influence degree of each factor on the occurrence of leakage (the leakage that can easily lead to accidents, rather than small leaks). Then, the probability of GPN leakage can be predicted under a specific scenario. This study can provide a reference for safety management of GPN to reduce risk and potential loss.

Suggested Citation

  • Qing Deng & Kuo Wang & Jiahao Wu & Feng Yu & Huiling Jiang & Lida Huang, 2023. "An integrated model for evaluating the leakage risk of urban gas pipe: a case study based on Chinese real accident data," 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. 116(1), pages 319-340, March.
  • Handle: RePEc:spr:nathaz:v:116:y:2023:i:1:d:10.1007_s11069-022-05676-2
    DOI: 10.1007/s11069-022-05676-2
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    References listed on IDEAS

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    1. Taleb-Berrouane, Mohammed & Khan, Faisal & Hawboldt, Kelly, 2021. "Corrosion risk assessment using adaptive bow-tie (ABT) analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    2. Peng Zhang & Guojin Qin & Yihuan Wang, 2018. "Optimal Maintenance Decision Method for Urban Gas Pipelines Based on as Low as Reasonably Practicable Principle," Sustainability, MDPI, vol. 11(1), pages 1-19, December.
    3. Zheng He & Negar Elhami Khorasani, 2022. "Identification and hierarchical structure of cause factors for fire following earthquake using data mining and interpretive structural modeling," 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. 112(1), pages 947-976, May.
    4. Zhang, Y. & Weng, W.G., 2020. "Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    5. Saeideh Farahani & Ahmad Tahershamsi & Behrouz Behnam, 2020. "Earthquake and post-earthquake vulnerability assessment of urban gas pipelines network," 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. 101(2), pages 327-347, March.
    6. Haifeng Bian & Jun Zhang & Ruixue Li & Huanhuan Zhao & Xuexue Wang & Yiping Bai, 2021. "Risk analysis of tripping accidents of power grid caused by typical natural hazards based on FTA-BN 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. 106(3), pages 1771-1795, April.
    7. Wu, Wei-Shing & Yang, Chen-Feng & Chang, Jung-Chuan & Château, Pierre-Alexandre & Chang, Yang-Chi, 2015. "Risk assessment by integrating interpretive structural modeling and Bayesian network, case of offshore pipeline project," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 515-524.
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