Assessment of Post-Disaster Community Infrastructure Services Demand Using Bayesian Networks

Abstract eng:
In this study a Bayesian Probabilistic Network (BPN) is presented to assess the community demand for the services provided by civil infrastructure systems during the absorption phase after a major earthquake. The post-disaster evaluation of the demand is a key component for assessing resilience in the compositional demand/supply resilience framework. The performance of the building stock is used as proxy for community infrastructure services demand. The damage states of the different building types, obtained through the hazard and fragility modules of the BPN, are associated with a change in service demand. The BPN is used to model the case of the electric power demand in Nepal after the 2015 Gorkha earthquake. Preliminary results are shown. BPNs have various advantages, including updatability with evidence. The probabilities can be traced through the different nodes. They are however computationally expensive and the required computational effort grows quickly with the number of nodes. The presented BPN can be adapted and used for other cities, infrastructure systems and seismic hazard conditions, and refined by implementing additional random variables that further characterize the fragility of the building stock.

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