Finding Width, Angle, Endpoint Length, and Actual Path Length of Cracks in Concrete Structures Using CNN and Image Processing

Abstract

The degradation of infrastructures such as bridges, highways, buildings, and dams has accelerated due to environmental and loading consequences. The most popular method for inspecting existing concrete structures has been visual inspection. Inspectors assess defects visually based on their engineering expertise, competence, and experience. This method, however, is subjective, tiresome, time-consuming, and constrained by the requirement for access to multiple components of complex structures. The angle, width, and length of the crack allow investigators to figure out the cause of the propagation and extent of the damage, and rehabilitation can be suggested based on that. This research proposes an algorithm based on a pre-trained convolutional neural network (CNN) and image processing to find the crack's angle, width, endpoint length, and actual path length in a concrete structure. The results show low relative errors of 2.19%, 14.88%, and 1.11% for the crack's angle, width, and endpoint length.