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Statistical learning techniques for the estimation of lifeline network performance and retrofit selection

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  • Wu, Jason
  • Baker, Jack W.

Abstract

The reliability of water supply networks subjected to catastrophic events is a crucial concern to communities, but our ability to assess these systems is limited by their size and complexity. This paper proposes a statistical learning technique, Random Forests, to efficiently estimate network performance in place of direct physical simulation. This technique uses a set of explanatory metrics that describe the impact of seismic damage to network behavior. The approach is applied to a case study network, the Auxiliary Water Supply System of San Francisco. The resulting statistical model is shown to replicate network performance estimates from flow-based hydraulic simulation, and exhibits good performance in identifying components to retrofit to improve the reliability of the system. The favorable performance and computational advantages of this approach make it an attractive tool for infrastructure reliability and risk mitigation analyses.

Suggested Citation

  • Wu, Jason & Baker, Jack W., 2020. "Statistical learning techniques for the estimation of lifeline network performance and retrofit selection," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:reensy:v:200:y:2020:i:c:s0951832019306933
    DOI: 10.1016/j.ress.2020.106921
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    References listed on IDEAS

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    Cited by:

    1. Hongyan Dui & Yuheng Yang & Yun-an Zhang & Yawen Zhu, 2022. "Recovery Analysis and Maintenance Priority of Metro Networks Based on Importance Measure," Mathematics, MDPI, vol. 10(21), pages 1-20, October.

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