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A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems

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  • Yu, Jin-Zhu
  • Whitman, Mackenzie
  • Kermanshah, Amirhassan
  • Baroud, Hiba

Abstract

Measuring the performance of infrastructure networks is critical to the allocation of resources before, during, and after a system’s disruption. However, the lack of data often hinders the ability to accurately estimate infrastructure performance, resulting in uncertainty in its evaluation which can lead to biased estimates. To address this challenge, this study develops a Bayesian approach to measure the performance of the infrastructure network at the component level and incorporate it in the evaluation of the system-level serviceability. Component fragility metrics are estimated using a hierarchical Bayesian model and then integrated into the system serviceability assessment using Monte Carlo simulation and a shortest-path algorithm. These performance measures can be dynamically updated as more data becomes available. A case study of the water distribution system of Shelby County in Tennessee subject to earthquake and flood hazards is presented to illustrate the proposed approach. Results show that system topology is more important in determining component functionality under seismic hazard while vulnerability is the dominant factor in the case of flood hazard.

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  • Yu, Jin-Zhu & Whitman, Mackenzie & Kermanshah, Amirhassan & Baroud, Hiba, 2021. "A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021002684
    DOI: 10.1016/j.ress.2021.107735
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    References listed on IDEAS

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    Cited by:

    1. Wu, Jingyi & Yu, Yang & Cheng, Siyuan & Li, Zhenmian & Yu, Jianxing, 2022. "Probabilistic multilevel robustness assessment framework for a TLP under mooring failure considering uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. Mehryar, Mehdi & Hafezalkotob, Ashkan & Azizi, Amir & Sobhani, Farzad Movahedi, 2023. "Dynamic zoning of the network using cooperative transmission and maintenance planning: A solution for sustainability of water distribution networks," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Fan, Xudong & Zhang, Xijin & Yu, Xiong Bill, 2023. "Uncertainty quantification of a deep learning model for failure rate prediction of water distribution networks," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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