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Consequence-based framework for buried infrastructure systems: A Bayesian belief network model

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  • Kabir, Golam
  • Balek, Ngandu Balekelayi Celestin
  • Tesfamariam, Solomon

Abstract

The failure of municipal buried infrastructures (potable water supply, wastewater systems, and stormwater systems) may cause crucial consequences to the environment, society, health, and economy. The buried infrastructure management has transformed from reactive to the preventive action plan. In this study, a Bayesian belief network (BBN) based buried infrastructure consequence model is developed to assess the consequence index and to prioritize the buried infrastructures for maintenance/ rehabilitation/ replacement. The causal relationships between different parameters are constructed based on published literature and expert knowledge. The proposed model can provide information at pipe level by estimating the health & safety impact, environmental impact, social impact, and economical & organizational impact due to failure. The proposed model is also capable of highlighting the most sensitive and vulnerable pipes within the network. The applicability of the proposed model is demonstrated on the wastewater collection network of the City of Vernon, BC. Results indicate that proposed BBN-based consequence model can explicitly quantify uncertainties and handle the nonlinear and sophisticated relationships between several factors.

Suggested Citation

  • Kabir, Golam & Balek, Ngandu Balekelayi Celestin & Tesfamariam, Solomon, 2018. "Consequence-based framework for buried infrastructure systems: A Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 290-301.
    • Handle: RePEc:eee:reensy:v:180:y:2018:i:c:p:290-301
    DOI: 10.1016/j.ress.2018.07.037
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    Cited by:

    1. Md. Rabbi & Syed Mithun Ali & Golam Kabir & Zuhayer Mahtab & Sanjoy Kumar Paul, 2020. "Green Supply Chain Performance Prediction Using a Bayesian Belief Network," Sustainability, MDPI, vol. 12(3), pages 1-19, February.
    2. Jeonghun Lee & Chan Young Park & Seungwon Baek & Seung H. Han & Sungmin Yun, 2021. "Risk-Based Prioritization of Sewer Pipe Inspection from Infrastructure Asset Management Perspective," Sustainability, MDPI, vol. 13(13), pages 1-21, June.
    3. 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).
    4. Iannacone, Leandro & Sharma, Neetesh & Tabandeh, Armin & Gardoni, Paolo, 2022. "Modeling Time-varying Reliability and Resilience of Deteriorating Infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    5. Yang, Zhuyu & Barroca, Bruno & Laffréchine, Katia & Weppe, Alexandre & Bony-Dandrieux, Aurélia & Daclin, Nicolas, 2023. "A multi-criteria framework for critical infrastructure systems resilience," International Journal of Critical Infrastructure Protection, Elsevier, vol. 42(C).
    6. Aalirezaei, Armin & Kabir, Dr. Golam & Khan, Md Saiful Arif, 2023. "Dynamic predictive analysis of the consequences of gas pipeline failures using a Bayesian network," International Journal of Critical Infrastructure Protection, Elsevier, vol. 43(C).
    7. Shen, Yang & Yang, Zhen & Guo, Li & Zhao, Xiaozhe & Duan, Yao, 2024. "Scenario mapping for critical infrastructure failure under typhoon rainfall: A dependency and causality approach," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    8. Liu, Jin & Zhai, Changhai & Yu, Peng, 2022. "A Probabilistic Framework to Evaluate Seismic Resilience of Hospital Buildings Using Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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