Prediction Model for Long-Term Bridge Bearing Displacement Using Artificial Neural Network and Bayesian Optimization

Bridge bearings are critical components in bridge structures because they ensure the normal functioning of bridges by accommodating long-term horizontal movements caused changing environmental conditions. However, abnormal structural behaviors displacement observed when integrity is degraded. This study aims to construct an accurate prediction model for under varying external conditions support reliable assessment which has not been fully explored previous studies. The main challenge developing lies modeling influencing factors that accurately simulate effect on displacement. To enhance accuracy proposed study, surrounding effects considering relationship between current and past displacements addition air temperature, thermal inertia, solar radiation modeled as factors. In addition, a data-driven method based artificial neural network (ANN) integrated with Bayesian optimization (BO) employed predict adopted An overpass equipped bearing monitoring temperature sensors used validate robustness effectiveness method. analysis results concludes can generate robust supports anomaly detection approach early warning systems structures.