A Bayesian network-based probabilistic framework for seismic vulnerability assessment of road networks

After an earthquake, roadside debris from collapsed buildings, structural damage to bridges, and large-scale emergency evacuations are major obstructions to the connectivity of road networks (RNs). However, few studies have comprehensively examined the impact of all these obstructions on the seismic vulnerability of RNs while considering uncertainties. In this paper, a novel probabilistic framework is proposed for seismic vulnerability assessment of RNs based on Bayesian networks (BNs). Herein, the vulnerability of RNs is defined as a combination of the failure probability of a road link and its conditional consequences in terms of network dis-connectivity. The framework can be used to assess the effects of debris distributions, the functionality losses of bridges, and evacuation flows on the failure probabilities of road links and the seismic vulnerability of RNs. Uncertainties in the post-earthquake damage states of buildings and bridges, building collapse types, building-to-road distances, and travel costs are explicitly considered and propagated in the framework using Monte Carlo (MC) simulations. The MC results are used to establish prior probabilities, and based on bidirectional inference with BNs, it is possible to assess the seismic vulnerability of RNs and identify critical roads. The proposed methodology is verified through a real-world RN in China.