Handa, Y;
Opabola, E;
Galasso, C;
(2024)
A Bayesian approach for estimating the post-earthquake recovery trajectories of electric power systems in Japan.
Sustainable and Resilient Infrastructure
10.1080/23789689.2024.2303801.
(In press).
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Abstract
Post-disaster recovery modelling of engineering systems has become an important facet of catastrophe risk modelling and management for natural hazards. The post-disaster recovery trajectory of a civil infrastructure system can be quantified using (a) the initial post-disaster functionality level, Qo; (b) rapidity, h (i.e., the rate of functionality restoration); and (c) recovery time, Rt. This study uses a Bayesian estimation approach to derive a set of probabilistic models to estimate Qo, Rt, and h of electric power networks (EPNs) using post-earthquake recovery data from 16 large earthquakes in Japan between 2003 and 2022. The considered predictor (explanatory) variables include earthquake magnitude, year of occurrence, seismic intensity, and exposed population (PEX). Apart from being a simple and efficient stand-alone tool, the proposed data-driven models can be a useful benchmarking tool for simulation-based approaches for EPN recovery modelling.
| Type: | Article |
|---|---|
| Title: | A Bayesian approach for estimating the post-earthquake recovery trajectories of electric power systems in Japan |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/23789689.2024.2303801 |
| Publisher version: | http://dx.doi.org/10.1080/23789689.2024.2303801 |
| Language: | English |
| Additional information: | © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
| Keywords: | Electric power network; resilience; post-earthquake; recovery; Bayesian parameter estimation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10186434 |
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