Seismic damage assessment of bonded versus unbonded laminated rubber bearings: A deep learning perspective

Laminated Rubber Bearings (LRBs), widely employed in highway bridges worldwide, are vulnerable to shear failure, squeezing or displacing during seismic events, potentially compromising bridge safety. Establishing a reliable approach for assessing the damage of LRBs is crucial for evaluating the seismic performance of bridges and preventing serious damage. To address this concern, this paper proposes a novel approach for LRBs seismic damage assessment based on computer vision (CV) technique. First, quasi-static loading tests are conducted on both bonded and unbonded LRBs to effectively enhance the understanding of their nonlinear properties. Subsequently, a large number of bearing samples are investigated to develop a comprehensive image database for a CV-based damage assessment model. A deep convolutional network (CNN) is designed to segment damage pixels, which are then combined with damage indicators to quantify the impact of various influencing factors on seismic damage using the interpretable machine learning technique, Shapley value. The results demonstrate that the CNN model achieves high-precision pixel segmentation and accurate predictions of seismic damage to LRBs, with RMSE consistently below 0.009 and R 2 exceeding 95 %. The primary factor influencing LRB damage is the type of constraint, which interacts with loading characteristics and geometric dimensions to produce various coupled effects. This high-precision CV-based approach shows significant potential for estimating seismic damage states of critical bridge components and building seismic protection.