Rapid earthquake loss updating of spatially distributed systems via sampling-based Bayesian inference
Gehl, Pierre and Fayjaloun, Rosemary and Sun, Li and Tubaldi, Enrico and Negulescu, Caterina and Özer, Ekin and D'Ayala, Dina (2022) Rapid earthquake loss updating of spatially distributed systems via sampling-based Bayesian inference. Bulletin of Earthquake Engineering, 20 (8). pp. 3995-4023. ISSN 1573-1456 (https://doi.org/10.1007/s10518-022-01349-4)
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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.
ORCID iDs
Gehl, Pierre, Fayjaloun, Rosemary, Sun, Li, Tubaldi, Enrico, Negulescu, Caterina, Özer, Ekin ORCID: https://orcid.org/0000-0002-7177-0753 and D'Ayala, Dina;-
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Item type: Article ID code: 79602 Dates: DateEvent30 June 2022Published4 March 2022Published Online3 February 2022AcceptedSubjects: Technology > Engineering (General). Civil engineering (General) > Environmental engineering Department: Faculty of Engineering > Civil and Environmental Engineering Depositing user: Pure Administrator Date deposited: 14 Feb 2022 16:41 Last modified: 25 Sep 2024 20:13 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/79602