Active learning–enhanced ensemble method for spatiotemporal correlation modeling of neighboring bridge behaviors to girder overturning

Abstract

Structural health monitoring (SHM) systems are widely deployed in transportation networks, yet traditional methods often focus on individual bridges, overlooking interdependencies between neighboring structures. This study proposes an active learning–enhanced ensemble learning model to predict the tilt behavior of adjacent bridges by leveraging critical response data from multiple bridges. The ensemble model integrates gradient boosting, random forest, and Gaussian process regressors, providing both predictive means and uncertainty quantification. Active learning iteratively selects the most informative samples, improving model efficiency and reducing data requirements. The model accurately predicts vertical displacement and tilt using responses from neighboring bridges, effectively capturing spatiotemporal correlations and dynamic interactions. Active learning achieves comparable accuracy with just 50% of traditional training samples, demonstrating its efficiency. The results reveal structural interdependencies influenced by stiffness and load distribution variations. The successful prediction of tilt behavior underscores the model’s potential for real-time SHM, early overturning warnings, and enhanced bridge safety.