A methodology for qualitative assessment of cracks in structural concrete using a combination of image processing techniques and knowledge-based models

Concrete structures are prone to degradation due to various internal flaws and external stresses, with crack formation being one of the most critical challenges affecting their strength and durability. Traditional methods of condition assessment are often limited by their inability to systematically detect and differentiate between crack types. In this study, a hybrid methodology is proposed in which manual crack assessment is complemented by classical image processing techniques, specifically, Otsu-Thresholding and Canny Edge Detection. Through this integration, the process of crack evaluation is automated and enhanced, allowing for more consistent identification and classification of cracks. The methodology is applied to real-world examples, where its effectiveness is demonstrated in detecting crack patterns at multiple scales and associating them with their underlying structural causes. It is shown that the proposed approach may provide a practical and resource-efficient tool for improving the consistency and reliability of structural assessments.