Deep learning–based defect detection in reinforced concrete deck slabs of highway bridges using impact sound analysis

Abstract

On Japan’s highway bridges, pavement and waterproofing layers are periodically replaced to maintain riding quality and prevent water intrusion into reinforced concrete (RC) deck slabs. During these works, inspectors perform hammer-sounding inspection by striking the concrete surface of RC deck slabs with a hammer and detecting defects from the resulting sound. However, this method is time-consuming, relies on inspector expertise and often yields inconsistent results. Therefore, a faster and more objective inspection approach is required. To address this challenge, we developed a defect classification model based on deep learning, using scalograms of impact sounds as feature representations. We also evaluated the relationship between model performance and computational cost to assess its feasibility for field inspections. The findings reveal a clear trade-off between accuracy and efficiency. Fine-tuning pre-trained convolutional neural networks (CNNs) consistently achieved high classification accuracy. Therefore, it is suitable when diagnostic precision is prioritized. However, this approach entails long training times. By contrast, a CNN–support vector machine (SVM) hybrid drastically reduced training time, though with a slight reduction in accuracy relative to fine-tuned CNNs. These results suggest that the choice of learning strategy should be tailored to inspection requirements. Fine-tuning is preferable when accuracy is critical, whereas the CNN–SVM hybrid is better suited for rapid on-site inspections. This flexible application of learning methods demonstrates considerable potential as a practical inspection method for RC deck slabs in highway bridges.