Void damage detection of rubber bearings in bridge structures under laboratory conditions using the active sensing method and BO-XGBoost algorithm

Abstract

In bridge engineering, void damage in rubber bearings refers to the phenomenon where gaps occur between the rubber bearing and the bridge structure due to factors such as aging, overloading, and earthquakes. Void damage can lead to abnormal stress distribution, bearing deformation failure, and uneven load transfer, thereby affecting the overall stability and safety of the bridge. Consequently, long-term monitoring of void damage is essential. However, methods such as manual inspection and computer vision have disadvantages, including high costs, lengthy processes, and limited monitoring scope. To address these challenges in void damage detection, this paper proposes a novel method that combines the active sensing approach with the Bayesian optimization (BO)-optimized Extreme Gradient Boosting (XGBoost) algorithm. A total of 1200 sets of experimental data under different void conditions were obtained through the active sensing method, and a high-precision void damage detection model based on the BO-XGBoost algorithm was proposed. The study found that the proposed void damage detection model achieved an Accuracy value of 95.0% on the test set, indicating that the combination of the active sensing method with the BO-XGBoost algorithm can effectively solve the problem of void damage detection in laminated rubber bearings.