An artificial intelligence approach to detection and assessment of concrete cracks based on visual inspection photographs

School of Civil & Environmental Engineering, University of the Witwatersrand, Johannesburg

^* Corresponding author: yunus.ballim@wits.ac.za

Abstract

This paper reports on the development of an artificial intelligence system, based on convolutional neural networks and machine learning algorithms to assess photographic images of concrete surfaces for the presence and characteristics of cracks. CNNs are deep learning techniques that are particularly useful for image categorization. An important challenge in the development of the system was to ensure that real cracks could be distinguished from non-crack features or profiles on the concrete surface. After development, the AI system was trained using 1900 images of cracked and non-cracked concrete surfaces. A further 1100 images were then used for validation and testing of the system. The images were segmented or pixelated in order to simplify the representation of the image and make it easier to locate objects and boundaries. The system was further developed to estimate the length and average width of cracks in an image. The testing protocols showed that the AI model was 99.6% accurate in classifying cracked and non-cracked images. Furthermore, the average error for calculation of crack length and crack width was 1.5% and 5% respectively. These results show good promise for development of a fully-fledged AI system to support inspection and maintenance of RC structures.