Predicting characteristics of cracks in concrete structure using convolutional neural network and image processing - Qayyum, Waqas; Ehtisham, Rana; Bahrami, Alireza; Mir, Junaid; Khan, Qaiser Uz Zaman; Ahmad, Afaq; Özkılıç, Yasin Onuralp (Frontiers in Materials, 2023)
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, inefficient, and constrained by the requirement for access to multiple components of complex structures. The angle, width, and length of the crack allow us 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 from the CNN and image processing method developed in this research. The actual path of the length is found to be 14.69% greater than the crack endpoint length. When calculating crack length, it is crucial to consider a crack's irregular shape and the likelihood that its actual path length will be greater than the direct distance between the endpoints. This study suggested measurement methods that precisely consider the crack's shape to estimate its actual path length. The proposed method could be embedded in an unmanned aerial vehicle for fully automated inspection based on image and video input.