Acoustic Event-Based Prestressing Concrete Wire Breakage Detection

Abstract

The integrity of concrete bridges, essential for public safety and infrastructure longevity, can be risked by the breakage of prestressed wires, potentially leading to catastrophic failures. In response to this challenge, this study introduces a novel approach to detect prestressed wire breakage by employing dynamic signal representations: the Short-Time Fourier Transform (STFT, a technique for time-frequency analysis) and Mel-frequency cepstrum coefficients (MFCCs, capturing the timbral aspects of sounds). Acoustic emission signals from two Italian bridges were collected and processed to extract relevant features using STFT and MFCCs. The study employs a multilayer perceptron (MLP) classifier enhanced with the MixUp data augmentation technique—a method that blends samples to enhance training data diversity and volume—addressing the challenge of limited data and improving model robustness. The promising results achieved by the MLP classifier in detecting prestressed wire breakages underscore its efficacy. These results highlight the method’s potential, specifically using MFCC, for integration into real-time bridge monitoring systems, offering an efficient solution for enhancing infrastructure safety.