Interpretable machine learning for quantifying environmental and operational impacts on structural responses in long-span bridges

Machine learning techniques have established sophisticated relationships between long-span bridge responses and environmental and operational variations (EOVs) for structural health monitoring (SHM) systems. Enhancing the interpretability of the machine learning models can significantly improve the reliability and trustworthiness of the SHM systems. This article proposes an interpretable machine learning framework designed to quantify the impacts of EOVs—specifically temperature variations, temperature gradients, and vehicle loads—on structural responses. Six linear and nonlinear machine learning models were evaluated to identify the most accurate and efficient model. Additionally, three interpretability techniques—regression coefficients, Permutation feature importance, and SHapley Additive exPlanations—were compared to determine the importance rankings of input temperature and vehicle load features on structural responses. The proposed framework was assessed using 21 months of continuous health monitoring data from a cable-stayed bridge. This research advances the application of interpretable machine learning in structural health monitoring.