Automated acoustic event‐based monitoring of prestressing tendons breakage in concrete bridges

Prestressing wire breakage induced by corrosion is hazardous, especially for concrete structures subjected to severe aging factors, such as bridges. Developing an automated monitoring system for such a damage event is therefore essential for ensuring structural integrity and preventing catastrophic failures. In line with this target, a supervised deep learning–based approach is proposed to detect and classify acoustic emissions released by prestressing wire breakage. The application of advanced signal processing techniques is central to this study to determine optimal model performance and accurately detect patterns of various events. Diverse pretrained convolutional neural network (CNN) architectures are explored and further enhanced by incorporating Bottleneck Attention Mechanisms to refine their performance capabilities. Additionally, a novel hybrid model, AcousticNet, tailored for acoustic event classification in the context of structural health monitoring, is developed. The models are trained and validated using an extensive data set collected from controlled laboratory experiments and in situ bridge monitoring scenarios, ensuring comprehensive adaptability and generalizability. The comprehensive analysis highlights that the Xception model, enhanced with a bottleneck module, and AcousticNet significantly outperform other models in capturing intricate patterns within acoustic signals. Integrating advanced CNN architectures with signal processing methods marks a substantial advancement in the automated monitoring of prestressed concrete bridges.