Probabilistic Framework for Evaluating Community Resilience: Integration of Risk Models and Agent-Based Simulation
Publication: Journal of Structural Engineering
Volume 146, Issue 11
Abstract
This paper proposes a novel probabilistic framework to quantitatively evaluate the resilience of communities comprising buildings and various interdependent infrastructure systems. To this aim, the proposed framework seamlessly integrates risk models and agent-based simulation in a Monte Carlo sampling scheme. The risk module includes models that evaluate the initial posthazard state of the community by probabilistic simulation of the hazard event, the structural response and damage of buildings and infrastructure systems, and cascading consequences that arise from interdependencies. Subsequently, the agent-based module simulates the recovery of the community from those consequences in which decentralized autonomous decision-making entities called “agents” undertake recovery operations. The agents prioritize buildings and infrastructure components for recovery and schedule operations as discrete events with uncertain duration and cost. Consequently, the probability distribution of the total cost incurred by the community and the total recovery time is evaluated. A resilience measure is then proposed as a function of the total community cost, which represents demand, and the gross regional product of the community, which represents the capacity to cope with that demand. The framework is showcased by a comprehensive application to a community comprising a portfolio of residential and commercial buildings, an electric power system, a water system, and a healthcare system subject to seismic hazard.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
The financial support from Iran National Science Foundation (INSF) through Grant No. 96013800 is gratefully acknowledged. The authors also thank Sharif University of Technology for Grant No. QA970110. The authors express their gratitude to Mr. Hassan Nasrazadani from Shanir Consultant Company and Dr. Hamed Kashani for insightful comments and recommendations. The authors thank Messrs. Sina Biazar and Hesam Alinejad from Sharif University of Technology for assisting with the latest revision of Rtx.
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Published In
Journal of Structural Engineering
Volume 146 • Issue 11 • November 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: May 13, 2019
Accepted: May 29, 2020
Published online: Aug 25, 2020
Published in print: Nov 1, 2020
Discussion open until: Jan 25, 2021
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