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Article dans une revue

Rapid earthquake loss updating of spatially distributed systems via sampling-based bayesian inference

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.
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Contributeur : Frédérique COUFFIGNAL Connectez-vous pour contacter le contributeur
Soumis le : jeudi 5 mai 2022 - 14:48:27
Dernière modification le : mercredi 3 août 2022 - 04:06:10

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Pierre Gehl, Rosemary Fayjaloun, Li Sun, Enrico Tubaldi, Caterina Negulescu, et al.. Rapid earthquake loss updating of spatially distributed systems via sampling-based bayesian inference. Bulletin of Earthquake Engineering, Springer Verlag, 2022, ⟨10.1007/s10518-022-01349-4⟩. ⟨hal-03660000⟩



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