AECWT-3DR-Net: Damage Localization Network for Concrete Structures Using Acoustic Emission Sensors

Abstract

In the construction industry, the deterioration of structures is a significant concern. To detect cracks in concrete structures, acoustic emission sensors are commonly used. The traditional approach relies on measuring the time of arrival, time difference of arrival, and received signal strength indicator. However, conventional methods are prone to error in the presence of inhomogeneous materials. In this research, we introduce a new method that employs deep learning techniques to detect cracks using acoustic emission sensors. The aim of this approach is to enhance the accuracy of crack detection while automating the process. The proposed method entails the following steps: capturing signals from acoustic emission sensors and then converting them into a time-frequency representation using continuous wavelet transform. These representations are fed into a convolutional neural network that is specifically designed to locate the crack. Finally, the convolutional neural network is trained to predict the coordinates of the crack. The proposed method's effectiveness and advancements were confirmed through experiments conducted on a concrete block that had a crack artificially created by pencil-lead breaks.