Machine learning application for concrete surface defects automatic damage classification

Abstract

Defects damage in concrete structures are an important measure of structural integrity and serviceability. In the context of investigating the condition of concrete surface that has defects, a visual inspection is usually performed. However, this method is subjective, tedious, time-consuming, and complicated, requiring access to many components of a large project design. Therefore, a Machine Learning classifier for concrete surface defect classification using the Discriminant Analysis Classifier was introduced to more accurately extract the types of concrete surface defects information from the digital images. The aim of this research is to increase the efficiency of concrete surface defect analysis in terms of quality, time and cost. 200 images were collected, with 50 images for each concrete defect (crack, corrosion, spalling, and no defect) serving as control data. The Gray Level Co-Occurrence Matrix (GLCM) is used to create an image processing and feature extraction algorithm. This model is trained using 80% of the image data and tested using another 20% of the image data. Thus, the model achieved 95% accuracy on the training data and 70% on the test data when using Quadratic Discriminant Analysis. These findings is very important to help engineers or construction inspectors in inspection activities.