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Dynamic risk assessment model of buried gas pipelines based on system dynamics

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  • Liu, Aihua
  • Chen, Ke
  • Huang, Xiaofei
  • Li, Didi
  • Zhang, Xiaochun

Abstract

The risks associated with buried gas pipeline are dynamic because of the variable operating environment as well as numerous accident-causing factors. However, most risk analysis studies provide a static overview of the system. This paper proposes a dynamic risk assessment model based on system dynamics (SD) to deal with both the complexity of a given system and changes therein with time, because SD offers unique advantages in revealing the dynamic characteristics of system behavior. For corrosion failure, which is closely related to time, our preliminary results are used to calculate the dynamic failure probability. For the time-independent failure causes, a failure probability calculation model based on the modification factors is proposed. Then, the accident consequences are analyzed according to the evolution process of gas accidents. The SD model for the risk assessment of gas pipelines is constructed by considering failure probability and accident consequences. The failure probability, accident consequences, and individual risk are simulated by considering a natural gas pipeline in Zhuhai, China, as an example. The results show that the dynamic development laws of buried gas pipeline risk are consistent with the actual situation and the proposed model can effectively characterize the temporal and spatial laws of risk evolution.

Suggested Citation

  • Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:reensy:v:208:y:2021:i:c:s095183202030819x
    DOI: 10.1016/j.ress.2020.107326
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    References listed on IDEAS

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    14. Yang, Kai & Hou, Lei & Man, Jianfeng & Yu, Qiaoyan & Li, Yu & Zhang, Xinru & Liu, Jiaquan, 2023. "Supply reliability analysis of natural gas pipeline network based on demand-side economic loss risk," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
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    16. Yin, Yuanbo & Yang, Hao & Duan, Pengfei & Li, Luling & Zio, Enrico & Liu, Cuiwei & Li, Yuxing, 2022. "Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    17. Ruiz-Tagle, Andres & Lewis, Austin D. & Schell, Colin A. & Lever, Ernest & Groth, Katrina M., 2022. "BaNTERA: A Bayesian Network for Third-Party Excavation Risk Assessment," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    18. Medeiros, Cristina Pereira & da Silva, Lucas Borges Leal & Alencar, Marcelo Hazin & de Almeida, Adiel Teixeira, 2021. "A new method for managing multidimensional risks in Natural Gas Pipelines based on non-Expected Utility," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    19. 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).

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