Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning

• The feasibility of using portable cameras for measuring concrete crack depth was studied. • A binary-class CNN model was utilized to detect the crack. • A regression algorithm integrating CNN , RF and XGBoost was developed to estimate the crack depth. • The efficiency of the proposed algorithms is discussed. Automatic inspection for crack detection and estimation of the crack depth is critical in assessing the damage and determining the appropriate method of repair in concrete structures. Most of the studies which have employed deep learning models for automatic inspection are limited to the detection and estimation of the width, length, area, and direction of cracks. The innovation of this study lies in developing a comprehensive automated crack detection and crack depth evaluation framework for concrete structures using images taken from portable devices. Firstly, a binary-class Convolutional Neural Network (CNN) model was developed to automatically detect the cracks on a concrete surface. Secondly, an integrated CNN model combining the convolutional feature extraction layers and regression models (RF and XGBoost) was developed to automatically predict the depth of the cracks. The proposed framework has been validated on a reinforced concrete (RC) slab. Results indicate the models are accurate and reliable for automated inspection of the cracks which could help in evaluating the condition of a concrete structure and choosing suitable repair methods.