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.

Systemic reliability of bridge networks with mobile sensing‐based model updating for postevent transportation decisions

“…There have been recent efforts on modeling spatially distributed civil infrastructures’ seismic performance. In terms of rapid loss assessment for postemergency service allocation, Bayesian network was utilized to update prior loss estimation using the GMPEs and the fragility curves of spatially distributed systems (Gehl et al., 2018, 2022). Such a framework was also applied for multihazard assessment (e.g., earthquakes and ground failures; Gehl & D'Ayala, 2016) and resiliency indicators through the restoration process (Gehl & D'Ayala, 2018) for bridge‐network systems.…”