Assessing seismic vulnerability of urban road networks by a Bayesian network approach

Evaluating the seismic vulnerability of urban road network is important for proactive reinforcement of the weakest link of the road network and post-disaster response and recovery of infrastructure systems. The paper proposes a Bayesian network (BN) method for seismic vulnerability assessment of an urban road network considering spatial seismic hazard with different levels of ground motion intensities, vulnerability of the components and effect of structural damage of components within the road network to the network functionality. Seismic vulnerability of the components in the road network is quantified based on physical structural damage under the earthquake demand. The vulnerability of the network is evaluated in terms of its dis-connectivity due to structural damage and induced transportation functional loss. BN model is built with vulnerability of the components as prior probability. The BN structure and CPT (Conditional Probability Table) is established based on transportation network analysis. The dis-connectivity of the road network is updated in light of information obtained from the observation of system or component states. Thus, the posterior probability could be calculated to identify the critical links and assess the seismic vulnerability of road network respectively. A case study is presented to show the application of the method. Results may contribute to the decision making on the reinforcement of the road network and the management of available resources to improve the disaster resilience and functionality of transportation systems in terms of seismic vulnerability.