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Bayesian updating for rapid earthquake loss assessment of road network systems

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). 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 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 : Pierre Gehl Connectez-vous pour contacter le contributeur
Soumis le : mercredi 10 août 2022 - 10:34:54
Dernière modification le : samedi 1 octobre 2022 - 03:24:47
Archivage à long terme le : : vendredi 11 novembre 2022 - 18:31:27


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  • HAL Id : hal-03748966, version 1



Pierre Gehl, Rosemary Fayjaloun, Li Sun, Enrico Tubaldi, Caterina Negulescu, et al.. Bayesian updating for rapid earthquake loss assessment of road network systems. 3rd EUROPEAN CONFERENCE ON EARTHQUAKE ENGINEERING & SEISMOLOGY, Sep 2022, Bucarest, Romania. ⟨hal-03748966⟩



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