Abstract
The resilience of a system is generally defined in terms of its ability to withstand external perturbations, adapt, and rapidly recover. This paper introduces a probabilistic formulation to predict the recovery process of a system given past recovery data and to estimate the probability of reaching or exceeding a target value of functionality at any time. A Bayesian inference is used to capture the changes over time of model parameters as recovery data become available during the work progress. The proposed formulation is general and can be applied to continuous recovery processes such as those of economic or natural systems, as well as to discrete recovery processes typical of engineering systems. As an illustration of the proposed formulation, two examples are provided. The paper models the recovery of a reinforced concrete bridge following seismic damage, as well as the population relocation after the occurrence of a seismic event when no data on the duration of the recovery are available a priori.
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Acknowledgments
This work was supported by the National Science Foundation (NSF) under Award No. 1638346 and by the National Institute of Standards and Technology (NIST) through the Center for Risk-Based Community Resilience Planning under Award No. 70NANB15H044. The research leading to these results has also received funding from the European Research Council under the Grant Agreement No. ERC_IDEAL RESCUE_637842 of the project IDEAL RESCUE-Integrated Design and Control of Sustainable Communities during Emergencies. Opinions and findings presented are those of the authors and do not necessarily reflect the views of the sponsors.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 5 • Issue 4 • December 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Apr 5, 2018
Accepted: Feb 26, 2019
Published online: Sep 21, 2019
Published in print: Dec 1, 2019
Discussion open until: Feb 21, 2020
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