An Efficient Bayesian Framework for Seismic Resilience Assessment and Maintenance Management of Similar Bridges

Efficient seismic resilience assessment and maintenance management are vital for bridge network functionality after post-earthquake. This study presents an integrated Bayesian framework for rapid and reliable evaluation of similar bridges using limited computational resources. By updating fragility and resilience characteristics based on prior information from a reference structure, the method generalizes component-level fragility assessment to network level. Application to continuous rigid-frame bridges with varying pier heights demonstrates deviations within 0.07 compared to conventional approaches while reducing computational effort by approximately 30%. A two-level maintenance strategy further prioritizes network-level and component-level decisions. The framework offers a practical, scalable solution for resilience-based bridge management.