Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning

This study focuses on developing and examining the effectiveness of Transfer Learning (TL) for structural health monitoring (SHM) systems that transfer knowledge about damage states from one structure (i.e., source domain) to another target domain). is an efficient method mapping domains. In addition, Proper Orthogonal Modes (POMs), which help classify behavior health, provide a promising tool identification in systems. Previous investigations show intensity location are highly correlated with POM variations structures under unknown loads. To train algorithms based POMs ML, generally needs use multiple simulations generate scenarios. The developed process applied simply supported truss span multi-span railway bridge. TL first used obtain relationships between two modeled bridges: being model labeled) other bridge unlabeled). technique then implemented develop damaged, using links POMs. It shown trained was effectively generalized other, somewhat similar, bridges population.