Condition assessment of concrete structures using automated crack detection method for different concrete surface types based on image processing

• Published in: Discover Civil Engineering 2024-09, Vol.1 (1), p.1-16, Article 81
• Main Authors: Shalaby, Yasmin M., Badawy, Mohamed, Ebrahim, Gamal A., Abdelalim, Ahmed Mohammed
• Format: Article
• Language: English
• Subjects: Bridge decks; Concrete cubes; Crack detection; Image processing; Inspection; Walls
• ISSN: 2948-1546; 2948-1546
• DOI: 10.1007/s44290-024-00089-5

Abstract In the inspection and diagnosis of concrete construction, crack detection is highly recommended in the earliest phases to prevent any potential risks later. However, the flaws in concrete surfaces cannot be reliably and effectively identified using traditional crack detection techniques. The suggested algorithm is a supportive tool for agents or authorities to use in crack detection mechanisms to monitor and assess the current condition of buildings or bridges. The researchers aim to establish an intelligent model for automatic crack detection on different concrete surfaces based on image processing technology. Three different concrete surfaces—bridge decks, walls, and concrete cubes—are used to test the model. A subset of the public dataset of bridge decks and walls from SDNET (2018) and 150*150*150 mm of concrete cubes taken from the material laboratory of the faculty of engineering at Ain Shams University are applied to the model. The model F1-score measures are 98.87%, 97.43%, and 74.11% for detecting cracks in bridges, walls, and concrete cubes, respectively. The validation of the applicability of the suggested novel approach is based on a comparison with recent methods for crack recognition. The contribution of this study is that it could be applied to detect cracks efficiently on three different types of concrete surfaces, including uneven concrete surfaces, random noise, voids, dents, colour changes, and stain marks. The proposed method is transparent in its workflow and has a lower computational cost compared with deep learning frameworks. Thus, the outcomes of this model demonstrate its effectiveness in concrete defect field investigation.