Amir Hossein Asjodi - M.Sc. Structural engineering, Sharif University
Kiarash M.Dolatshahi - Associate professor, Sharif University
Mohammad Javad Daeizadeh - M.Sc. Structural engineering, Sharif University

Although manual crack inspection has been widely used for structural health monitoring over the last decades, the development of computer vision methods allows continuous monitoring and compensates the human judgment inaccuracy. In this study, an image-based method entitled, Arc Length method is introduced for extracting crack pattern characteristics, including crack width, and crack length. The method contains two major steps; in the first step, the crack zones are estimated in the whole image. Afterwards, the algorithm finds the start point, follows the crack pattern, and measures the crack features, such as crack width, crack length, and crack pattern angle. The application of this approach plays a significant role in the maintenance and crack monitoring of infrastructures, such as concrete bridges and tunnels.

Image processing, crack width, crack length, image recognition, surface crack pattern