Chloride-induced concrete deterioration monitoring using advanced ultrasonic pulse wave analysis based on convolutional neural network

Abstract

This research explores the potential of deep learning techniques, specifically the convolutional neural network (CNN) architecture, for classifying concrete crack levels based on an acceptable threshold of concrete cracking. The classification model utilizes ultrasonic pulse wave data collected from concrete cube specimens before and after undergoing an accelerated corrosion process. A total of 108 concrete specimens, representing three different mix designs, three corrosion levels, and four concrete cover thicknesses, were utilized in this study. The collected data was employed to train CNN models, specifically leveraging the GoogLeNet and SqueezeNet architectures. Various input sampling rates, input lengths, and hyperparameters were explored to determine the optimal training setup, yielding the best prediction performance. The results demonstrate that the optimized models achieve an 84% accuracy in distinguishing cracks below and above the acceptable threshold. Therefore, it can be concluded that the CNN method holds potential for in-situ sensors aimed at monitoring chloride-induced deterioration in concrete structures.