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

Abstract

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