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Bayesian network model for predicting probability of third-party damage to underground pipelines and learning model parameters from incomplete datasets

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  • Xiang, W.
  • Zhou, W.

Abstract

Damage caused by third-party excavation is one of the leading threats to the structural integrity of underground energy pipelines. Based on fault tree models reported in the literature, the present study develops a Bayesian network (BN) model to estimate the probability of a given pipeline being hit by third-party excavations by taking into account common protective and preventative measures. The Expectation-Maximization (EM) algorithm in the context of the parameters learning is employed to learn the parameters of the BN model from datasets that consist of individual cases of third-party activities but with missing information. The effectiveness of the parameter learning for the developed Bayesian network is demonstrated by a numerical example involving simulated datasets of third-party activities and a case study using real-world datasets obtained from a major pipeline operator in Canada. The BN model and EM-based parameter learning proposed in this study allow pipeline operators to estimate the probability of hit by efficiently taking into account historical third-party excavation records in an objective, efficient manner.

Suggested Citation

  • Xiang, W. & Zhou, W., 2021. "Bayesian network model for predicting probability of third-party damage to underground pipelines and learning model parameters from incomplete datasets," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:reensy:v:205:y:2021:i:c:s0951832020307614
    DOI: 10.1016/j.ress.2020.107262
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    1. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2011. "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 925-932.
    2. Yang, Yongsheng & Khan, Faisal & Thodi, Premkumar & Abbassi, Rouzbeh, 2017. "Corrosion induced failure analysis of subsea pipelines," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 214-222.
    3. Kelly, Dana & Atwood, Corwin, 2011. "Finding a minimally informative Dirichlet prior distribution using least squares," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 398-402.
    4. Smitha D. Koduru & Maher A. Nessim, 2017. "Review of Quantitative Reliability Methods for Onshore Oil and Gas Pipelines," Springer Series in Reliability Engineering, in: Paolo Gardoni (ed.), Risk and Reliability Analysis: Theory and Applications, pages 67-95, Springer.
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    Cited by:

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    5. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    6. 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).
    7. Bibartiu, Otto & Dürr, Frank & Rothermel, Kurt & Ottenwälder, Beate & Grau, Andreas, 2021. "Scalable k-out-of-n models for dependability analysis with Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    8. 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).
    9. Wang, WuChang & Zhang, Yi & Li, YuXing & Hu, Qihui & Liu, Chengsong & Liu, Cuiwei, 2022. "Vulnerability analysis method based on risk assessment for gas transmission capabilities of natural gas pipeline networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    10. 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.

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