Deep Learning for Assessing Severity of Cracks in Concrete Structures

Most concrete structures suffer from degradation, where cracks are the most obvious visual sign. Concrete structures must be continuously monitored and assessed to avoid further deterioration, which may lead to a partial or total collapse. This is particularly important when constructing large structures such as towers, bridges, tunnels, and dams. This work aims to demonstrate and evaluate several deep learning approaches that can be used for monitoring and assessing the level of concrete degradation based on the cracks’ visual signs, which can then be embedded in Health Monitoring Systems (SHM). The experimental work in this study involves creating three models: Two were built using ResNet-50 and Xception transfer learning networks. In contrast, the third was built using a customized Sequential Convolutional Neural Network (SCNN) architecture. The dataset comprises 2,000 image samples sampled from a larger dataset that contains 56,000 images and which belong to four severity classes: minor, moderate, and severe, in addition to a normal class for no crack signs. The SCNN model achieved an accuracy of 90.2%, while the Xception and ResNet-50 models scored an accuracy of 86.3% and 70%, respectively.