Single-sensor Acoustic Emission Damage Location in Orthotropic Steel Decks Based on Continuous Wavelet Transform and Convolutional Neural Network

Abstract

Orthotropic steel decks (OSDs) have found extensive application in long span bridges, owing to their excellent mechanical performance and constructional convenience. However, the presence of fatigue cracks remains a persistent threat to long-term structural safety. It is thus imperative to develop highly efficient and accurate methods for fatigue crack identification and location. In this context, a single-sensor acoustic emission (AE) damage location framework is developed based on continuous wavelet transform (CWT) and convolutional neural network (CNN), which leverages the high sensitivity of AE and the strong learning ability of artificial intelligence. CWT helps to represent the all-around propagation characteristics of AE signals in the time–frequency domain. CNN model then extracts those location sensitive features from CWT maps and predicts the location of AE damage sources. A field pencil lead break (PLB) experiment was conducted on an OSD segment in the operating status of the long-span bridge to validate the performance of the developed method, where a series of CNN models, including AlexNet, VGG19, and ResNet18, were established and compared. Results show that the framework based on CWT and ResNet18 could achieve satisfactory location accuracy with a single AE sensor, demonstrating significant potential for large-scale engineering structures.