Detection of void damage in laminated rubber bearings based on a fine-tuned YOLOv5s model

Abstract

As a seismic isolation device, laminated rubber bearings are widely used in bridges. With increasing service life and under the effects of vehicular vibrations, earthquakes, and construction quality issues, these bearings frequently develop void damage, which poses risks to structural safety. However, manual inspection suffers from untimely detection, heavy workload, limited accuracy, and safety hazards. To rapidly and accurately detect the degree of void damage in bearings, this study proposes an innovative detection method combining the active sensing method, continuous wavelet transform (CWT), and a fine-tuned YOLOv5s model. A total of 2064 sets of detection signals from laminated rubber bearings in different void degrees were obtained using the active sensing method, and CWT was applied to convert the one-dimensional signals into two-dimensional time–frequency images. Subsequently, the pre-trained YOLOv5s model was fine-tuned to optimize its adaptability for void degree detection. Building on this foundation, a high-accuracy detection model was established. Across the training, validation, and test sets, all four metrics are identical, with accuracy = 100% and precision = recall = F1-score = 1.0. This indicates that the model’s predictions perfectly match the true labels. Compared with models trained on one-dimensional data or on two-dimensional time–frequency images, the proposed model exhibits a significant improvement in predictive performance. The proposed method shows strong potential for void damage detection in bridge bearings, enabling timely and high-accuracy diagnosis and thereby contributing to the safety of bridge structures.