Predictive Modelling of bridge bearing displacements with Physics‐Enhanced Machine Learning (PEML) environmental effects filtering

Abstract In bridge Structural Health Monitoring (SHM), identifying anomalies is challenging due to environmental and operational variability (EOV), such as temperature changes, traffic loads, and else. This study develops a predictive model to isolate normal structural responses, enabling the detection of damage‐induced anomalies. Using displacement and temperature sensors, the model evaluates longitudinal displacements at the bridge bearings. Temperature is the primary independent variable, combined with time, to capture daily and seasonal cycles characterised by nonlinear behaviour. Regression‐based Machine Learning algorithms, such as Gaussian Process Regression (GPR), are employed to predict the expected displacements. A Physics‐Enhanced Machine Learning (PEML) approach, or grey‐box model, integrating physical knowledge with data‐driven insights is adopted, improving accuracy and interpretability. Tested on real‐world data from a highway viaduct, the grey‐box model demonstrates superior performance and robustness, even with limited datasets. This confirms the potential of PEML‐based approaches for damage assessment with data from static monitoring, paving the way for more reliable SHM systems and enhanced bridge safety.