Concrete Crack Detection, Orientation and Measurement Using a Wall Climbing Robot

Abstract

Concrete structure damage and severity are determined by examining the width of cracks in various forms. This categorisation of cracks is based on their specific types, which is crucial for engineers and professionals. It allows them to efficiently prioritise maintenance and repair efforts, extending the lifespan of concrete structures. Thus, the classification of cracks is based on the type of cracks needed. This study combines a wall-climbing robot equipped with imaging capabilities to automate the detection and classification of cracks in concrete surfaces. We used machine learning to classify the crack orientation and OTSU to segment the crack shapes. Based on this study, four experiments were carried out using machine learning methods to classify types of cracks, which are SVM, Random Forest, KNN, and Decision Tree. These experiments were categorised using multiclass classification with types of the orientation of crack such as Not crack, Crocodile, Transverse, and Longitudinal. The classification one-class results show that the Decision Tree achieved 86.50%, SVM 99.50%, Random Forest 97%, and KNN 40%. In multiclass classification, Decision Tree achieved 64%, Random Forest 80%, and KNN 37%. The higher accuracy from SVM is achieved at 87%.

Keywords

• artificial intelligence
• crack detection
• image processing
• machine learning
• wall climbing robot