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.
Highlights
Rapid loss assessment following an earthquake event is of utmost importance for crisis managers, in order to quantify the extent of the disaster and localize ‘hot-spots’ before planning adequate emergency measures
The complete Bayesian Networks (BNs) is solved in less than half an hour with a medium-performance personal computer (8 GB memory): this timeframe is in line with what should be expected from elaborate rapid response systems, it could be further improved with the use of dedicated computing servers
This study has investigated the implementation of a Bayesian Network-based framework for improving situational awareness during the rapid response phase following an earthquake event
Summary
Rapid loss assessment following an earthquake event is of utmost importance for crisis managers, in order to quantify the extent of the disaster and localize ‘hot-spots’ before planning adequate emergency measures. These considerations have led to the development of an approximate BN approach (Gehl et al 2018), where an incomplete naïve formulation (i.e., a set of component nodes converging towards the system node) is learned from off-line stochastic loss scenarios: less straightforward to implement, this method is able to deal with large real-world systems and to estimate elaborate system performance measures (i.e., not limited to connectivity analyses) This approach is based on the sampling of numerous earthquake events in the exposed area, based on the regional seismicity, which is not required in a rapid response context where the earthquake parameters (location, magnitude) are usually known with reasonable accuracy within minutes (Cremen and Galasso 2020).
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