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Risk assessment of buried gas pipelines based on improved cloud-variable weight theory

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  • Chen, Yinuo
  • Xie, Shuyi
  • Tian, Zhigang

Abstract

Various risk factors of urban gas pipelines will lead to leakage, significantly impacting the environment, assets, and life safety, thus making pipeline risk prevention and control crucial. However, existing methods cannot sufficiently model the uncertainty in the pipeline risk evaluation process. In addition, the calculation method of factor weights may lead to unrealistic results. In this study, variable weight theory and cloud theory are introduced. Then, a novel method of cloud-variable weight function is proposed to analyze the pipeline's risk level and critical risk factors by establishing a pipeline risk assessment index system. The proposed method fully considers the uncertainty in the evaluation process, resolves the contradiction of existing methods to model the fuzzy concepts accurately, optimizes the weight distribution, and obtains a more scientific and reasonable assessment result. A case study is conducted on a pipeline in Beijing City. The results illustrate that the proposed method is beneficial for helping pipeline operators determine the pipeline risk status and weak links in the pipeline system, thereby providing a basis for risk control and rehabilitation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:221:y:2022:i:c:s0951832022000515
    DOI: 10.1016/j.ress.2022.108374
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    Cited by:

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    2. Chen, Zhanfeng & Li, Xuyao & Wang, Wen & Li, Yan & Shi, Lei & Li, Yuxing, 2023. "Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Xie, Shuyi & Huang, Zimeng & Wu, Gang & Luo, Jinheng & Li, Lifeng & Ma, Weifeng & Wang, Bohong, 2024. "Combining precursor and Cloud Leaky noisy-OR logic gate Bayesian network for dynamic probability analysis of major accidents in the oil depots," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Aorui Bi & Shuya Huang & Xinguo Sun, 2023. "Risk Assessment of Oil and Gas Pipeline Based on Vague Set-Weighted Set Pair Analysis Method," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    5. Ibrahim Mutambik, 2024. "Assessing Urban Vulnerability to Emergencies: A Spatiotemporal Approach Using K-Means Clustering," Land, MDPI, vol. 13(11), pages 1-22, October.
    6. Dasgupta, Agnimitra & Johnson, Erik A., 2024. "REIN: Reliability Estimation via Importance sampling with Normalizing flows," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Chen, Yinuo & Tian, Zhigang & He, Rui & Wang, Yifei & Xie, Shuyi, 2023. "Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

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