A comprehensive approach in post-earthquake blockage prediction of urban road network and emergency resilience optimization

Yaser Hosseini, Reza Karami Mohammadi and Tony Y. Yang

Reliability Engineering and System Safety, 2024, vol. 244, issue C

Abstract: This study presents a probabilistic method for predicting urban road blockages due to the collapse of roadside buildings and develops network restoration planning through resilience optimization. Resilience analysis of a blocked network has faced two challenges: (1) blocked paths opening time estimation; and (2) network time-varying functionality evaluation. Probabilistic models, including earthquake magnitude, fault rupture location, seismic intensity, damage, debris, blockage, and debris evacuation models are utilized to determine the probability distribution of blocked paths opening time considering relevant uncertainties. A generalized network functionality metric is developed to evaluate network performance considering emergency multi-nodes such as medical centers. The optimization algorithm based on Simulated Annealing is extended for network restoration by assuming multi-location for resources pre-positioning. Three restoration scenarios are defined to identify required resources for network recovery with different restoration objectives. The proposed methodology is implemented in Region 2 of the Tehran metropolis. The results demonstrate that scenario (II) gives the highest number of required resources, so it is suitable where adequate resources are available. Scenario (III) predicts the least number of required resources; hence, it is appropriate where sufficient resources are not accessible. Subsequently, scenario (I) is an intermediate approach to determine the required resources with the lowest computational effort.

Keywords: Disaster management; Earthquake; Emergency response; Network recovery; Resilience; Road blockage (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:244:y:2024:i:c:s0951832023008013

DOI: 10.1016/j.ress.2023.109887

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