Work

Active Learning–Enhanced Ensemble Method for Spatiotemporal Correlation Modeling of Neighboring Bridge Behaviors to Girder Overturning

Year: 2025

Type: article

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.

Source: Structural Control and Health Monitoring

DOI: 10.1155/stc/6047080

Authors: Ru An, Mengjin Sun, You Dong, Lu Guo, Lei Jia, Xiaoming Lei

Institutions: University of Electronic Science and Technology of China, Shenzhen Urban Transport Planning Center Co., Shanghai Research Institute of Building Sciences (China), Hong Kong Polytechnic University

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,490