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Surrogate-based decision-making for post-earthquake recovery scheduling and resilience assessment of subway systems considering the effect of infrastructure interdependency

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  • Hu, Jie
  • Wen, Weiping
  • Zhai, Changhai
  • Pei, Shunshun

Abstract

This study proposes a method for post-earthquake recovery and seismic resilience assessment of subway systems considering the effect of infrastructure interdependency. A two-stage recovery decision-making model is established. In the pre-earthquake stage, reserved restoration resources are determined by balancing recovery speed and recovery efficiency. In the post-earthquake stage, the optimal restoration schedules are obtained by solving the established functional recovery optimization model using the NSGA-II. A deep learning-based surrogate model for functionality computation is developed to significantly reduce the computational costs in the recovery decision-making and optimization process. A seismic resilience assessment and recovery decision-making process is conducted for a real, large-scale subway system. The results indicate that the subway system exhibits high recovery efficiency in its early-stage recovery and a high level of seismic resilience. The study highlights the significant impact of the municipal power supply system (MPSS) as a critical infrastructure system. The recovery time affected by the MPSS accounts for more than 33% of the total recovery time of the subway system. Ignoring the impact of the MPSS led to an average 12.13% underestimation of the center of resilience. These results underscore the substantial influence of the MPSS on the recovery and seismic resilience of subway systems.

Suggested Citation

  • Hu, Jie & Wen, Weiping & Zhai, Changhai & Pei, Shunshun, 2025. "Surrogate-based decision-making for post-earthquake recovery scheduling and resilience assessment of subway systems considering the effect of infrastructure interdependency," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:reensy:v:256:y:2025:i:c:s0951832024008524
    DOI: 10.1016/j.ress.2024.110781
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