Application of convolutional neural network for detecting concrete cracks
Deep learning continues to growing in popularity and expanding for civil engineering applications thanks to easy access to massive sets of labeled data, increased computing power, and the availability of pre-trained models built by experts. In this paper, a Convolutional Neural Network (CNN) method is employed to classify the crack/noncrack aerial images captured on the surface of concrete structures. The CNN model was trained and validated using the available experimental data of 4000 previously published images. The trained CNN model was then tested with 330 unseen images. It was shown that the proposed CNN model can classify the crack/non-crack images with an accuracy level of 93%.