%0 Journal Article %T Rapid earthquake loss updating of spatially distributed systems via sampling-based bayesian inference %+ Bureau de Recherches Géologiques et Minières (BRGM) (BRGM) %+ University College of London [London] (UCL) %+ University of Strathclyde [Glasgow] %A Gehl, Pierre %A Fayjaloun, Rosemary %A Sun, Li %A Tubaldi, Enrico %A Negulescu, Caterina %A Özer, Ekin %A D’ayala, Dina %< avec comité de lecture %@ 1570-761X %J Bulletin of Earthquake Engineering %I Springer Verlag %8 2022-03-04 %D 2022 %R 10.1007/s10518-022-01349-4 %Z Sciences of the Universe [physics]/Earth SciencesJournal articles %X 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. %G English %2 https://brgm.hal.science/hal-03660000/document %2 https://brgm.hal.science/hal-03660000/file/s10518-022-01349-4.pdf %L hal-03660000 %U https://brgm.hal.science/hal-03660000 %~ BRGM