Gehl, P;
Fayjaloun, R;
Sun, L;
Tubaldi, E;
Negulescu, C;
Özer, E;
D’Ayala, D;
(2022)
Rapid earthquake loss updating of spatially distributed systems via sampling-based bayesian inference.
Bulletin of Earthquake Engineering
10.1007/s10518-022-01349-4.
(In press).
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Abstract
Within moments following an earthquake event, observations collected from the affected area can be used to define a picture of expected losses and to provide emergency services with accurate information. A Bayesian Network framework could be used to update the prior loss estimates based on ground-motion prediction equations and fragility curves, considering various field observations (i.e., evidence). While very appealing in theory, Bayesian Networks pose many challenges when applied to real-world infrastructure systems, especially in terms of scalability. The present study explores the applicability of approximate Bayesian inference, based on Monte-Carlo Markov-Chain sampling algorithms, to a real-world network of roads and built areas where expected loss metrics pertain to the accessibility between damaged areas and hospitals in the region. Observations are gathered either from free-field stations (for updating the ground-motion field) or from structure-mounted stations (for the updating of the damage states of infrastructure components). It is found that the proposed Bayesian approach is able to process a system comprising hundreds of components with reasonable accuracy, time and computation cost. Emergency managers may readily use the updated loss distributions to make informed decisions.
| Type: | Article |
|---|---|
| Title: | Rapid earthquake loss updating of spatially distributed systems via sampling-based bayesian inference |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10518-022-01349-4 |
| Publisher version: | https://doi.org/10.1007/s10518-022-01349-4 |
| Language: | English |
| Additional information: | © 2022 Springer Nature Switzerland AG. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Bayesian inference, Critical infrastructure, Seismic risk, Loss updating, Road network |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10145172 |
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