Probabilistic framework for evaluating the seismic resilience of transportation systems during emergency medical response

Mehdi Taghizadeh, Mojtaba Mahsuli and Hossain Poorzahedy

Reliability Engineering and System Safety, 2023, vol. 236, issue C

Abstract: This paper proposes a probabilistic framework for evaluating the seismic resilience of transportation systems during emergency medical response. The framework computes total social losses incurred by the community in terms of casualties and fatalities by seamless integration of probabilistic hazard, risk, and agent-based modules. The hazard module characterizes the earthquake event in terms of ground-shaking and ground-failure intensities. Subsequently, the risk module evaluates the initial post-earthquake state of the community by estimating the response and damage of various structures and infrastructure components, volume of debris, casualties, and immediate fatalities. The agent-based module simulates emergency response operations comprising search and rescue, injury transport and treatment, hospital functionality restoration, and road debris removal. This module also quantifies the time-variant demand and capacity of the network and emergency medical services. A computationally efficient network model simulates the time-variant network congestions, which is then used to compute the travel time of ambulances and ensuing additional fatalities. The primary results are the probability distributions of the community social loss, emergency response period, community resilience measure, and hospital accessibility. The framework identifies the most vulnerable zone for hospital accessibility, the most effective bridge retrofit plan, and the most effective emergency response plan to mitigate social losses.

Keywords: Resilience; Earthquake; Emergency medical response; Transportation network; Traffic modeling; Agent-based modeling (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:236:y:2023:i:c:s0951832023001709

DOI: 10.1016/j.ress.2023.109255

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