A probabilistic graphical approach for rapid state assessment of urban infrastructure systems under disasters

Haoran Zhu, Yihang Zhu, Tao Chen, Jiakun Dai, Lida Huang and Guofeng Su

Reliability Engineering and System Safety, 2025, vol. 264, issue PA

Abstract: State assessment is the first step of emergency response. However, it is difficult to timely evaluate the situations because of the lack and confusion of information. Therefore, this paper conceptualizes urban infrastructure systems through a probabilistic graphical paradigm and proposes an approach for rapid assessment under extreme disasters. Using the power transmission system in Puerto Rico as a case study, this paper describes the probabilistic graphical modelling procedure and the state assessment process based on the belief propagation algorithm. The results show that the accuracy of the belief propagation method based on probabilistic graphs is increased by 30 % compared to traditional methods. In addition, this paper also designs the node importance metrics from the perspective of information dissemination and compares them with traditional centrality based metrics under different disaster intensities. The study proposes a novel probabilistic assessment method for the state of infrastructure systems, which can be utilized to enhance the resilience of such systems.

Keywords: Infrastructure systems; Probabilistic graph theory; Belief propagation; Rapid assessment; Node importance metrics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:264:y:2025:i:pa:s0951832025005472

DOI: 10.1016/j.ress.2025.111346

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