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

Qing Deng, Kuo Wang, Jiahao Wu, Feng Yu, Huiling Jiang and Lida Huang ()
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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

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 116, issue 1, No 14, 319-340

Abstract: 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.

Keywords: Urban gas pipeline network; Leakage; Risk assessment; Bayesian network; Real accident data analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-022-05676-2

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