Enhancing Seismic Resilience of Bridge Infrastructure Using Bayesian Belief Network Approach

The deteriorating state of North America’s bridge infrastructure is a pressing issue, necessitating innovative risk management strategies. This study aims to enhance the seismic resilience of bridge infrastructure using a Bayesian belief network (BBN) model. The research uses literature review, expert opinions, and a Bayesian analysis framework to quantify bridge resilience, despite the scarcity of detailed historical data. The model, supported by conditional probability tables (CPTs), captures the complex interdependencies among parameters and uncertainties in seismic resilience assessment. Preliminary findings show that integrating expert judgment with BBN provides a robust methodology for assessing and enhancing bridge resilience to seismic hazards. This approach contributes to measuring bridge infrastructure resilience and offers practical guidance for policymakers, engineers, and stakeholders in sustainable transportation network development.