Evaluation of ASR in concrete using acoustic emission and deep learning

Abstract

Alkali-silica reaction (ASR) is one of main damages causes in concrete structures such as nuclear power plants which may endanger structural serviceability and integrity. Acoustic emission (AE) is a passive nondestructive method for structural health monitoring. It is very sensitive and has the capability of monitoring structures continuously. This method may be an alternative for early damage detection in concrete nuclear structures affected by ASR. The innovation of this paper lies in the implementation of deep learning algorithms to evaluate the ASR progress. ASR was monitored by AE in a concrete specimen, which was cast with reactive coarse aggregates and reinforced by steel rebars. The AE signals recorded during the experiment were filtered and divided into two classes. Two deep learning algorithms of convolutional neural network (CNN) and stacked autoencoder were employed to classify the AE signals into the corresponding classes. The model based on CNN resulted in a classifier with higher accuracy than the model based on the autoencoder network.