Identification of Corroded Cracks in Reinforced Concrete Based on Deep Learning SCNet Model

Abstract

ABSTRACT In order to improve the efficiency and accuracy of corroded cracks detection and classification in reinforced concrete, a corroded cracks identification model Steel Corrosion Net (SCNet), based on deep learning Convolutional Neural Network (CNN), is proposed. Crack figures are collected by self-shooting, internet search and corrosion test, then the data set of 39,000 pictures is built by data enhancement. Afterward, a SCNet three-classification neural network model is built and tested using TensorFlow learning framework and Python. The SCNet combines massive initial data with a multi hidden layer neural network framework, and achieves feature learning and accurate classification through model training. According to the training and testing accuracy of the model, the structure and parameters of the SCNet network are optimized. The results of SCNet are compared with those obtained by two traditional testing methods. The results show that the proposed SCNet model achieves a classification accuracy of 96.8%, so it can effectively identify and classify the corroded cracks in reinforced concrete, with high accuracy and measurability. Under harsh condition of noise interference, such as shadows and distortions, the proposed SCNet model shows a relatively stable classification performance compared with two traditional methods.