Failure due to cracks is a major structural safety issue for engineering constructions. Human examination is the most common method for detecting crack failure, although it is subjective and time-consuming. Inspection of civil engineering structures must include crack detection and categorization as a key component of the process. Images can automatically be classified using convolutional neural networks (CNNs), a subtype of deep learning (DL). For image categorization, a variety of pre-trained CNN architectures are available. This study assesses seven pre-trained neural networks, including GoogLeNet, MobileNet-V2, Inception-V3, ResNet18, ResNet50, ResNet101, and ShuffleNet, for crack detection and categorization. Images are classified as diagonal crack (DC), horizontal crack (HC), uncracked (UC), and vertical crack (VC). Each architecture is trained with 32,000 images equally divided among each class. A total of 100 images from each category are used to test the trained models, and the results are compared. Inception-V3 outperforms all the other models with accuracies of 96%, 94%, 92%, and 96% for DC, HC, UC, and VC classifications, respectively. ResNet101 has the longest training time at 171 min, while ResNet18 has the lowest at 32 min. This research allows the best CNN architecture for automatic detection and orientation of cracks to be selected, based on the accuracy and time taken for the training of the model.

Potholes are a significant hazard, causing severe damage to vehicles and potentially leading to fatal accidents. The automatic detection of potholes is cru

These defects can occur in the form of cracks, pain deterioration, dampness, etc. due to mechanical and weathering effects. Crack identification and categorization must be part of the inspection procedure for civil engineering structures. Convolutional neural networks (CNNs), a sub-type of deep learning (DL), can automatically classify the defective images of wooden structures. In this research, 10 pre-trained models of CNN, including ResNet18, ResNet50, ResNet101, Inception-V3, GoogleNet, ShuffleNet, InceptionResNet-V2, MobileNet-V2, XceptionNet, and NASNet-Mobile are evaluated for classification and prediction of defects in wooden structures. After prediction of class, the algorithm calculated the angle, length, and width of cracks with quantification errors of 0.15%, 1.54%, and 4.2% for first testing image, and 2.3%, 0.99%, and 2% for second testing image. Request access from your librarian to read this chapter's full text.

Potholes are fatal and can cause severe damage to vehicles as well as can cause deadly accidents. In South Asian countries, pavement distresses are the primary cause due to poor subgrade conditions, lack of subsurface drainage, and excessive rainfalls. The present research compares the performance of three pre-trained Convolutional Neural Network (CNN) models, i.e., ResNet 50, ResNet 18, and MobileNet. At first, pavement images are classified to find whether images contain potholes, i.e., Potholes or Normal. Secondly, pavements images are classi-fied into three categories, i.e., Small Pothole, Large Pothole, and Normal. Pavement images are taken from 3.5 feet (waist height) and 2 feet. MobileNet v2 has an accuracy of 98% for detecting a pothole. The classification of images taken at the height of 2 feet has an accuracy value of 87.33%, 88.67%, and 92% for classifying the large, small, and normal pavement, respectively. Similarly, the classification of the images taken from full of waist (FFW) height has an accuracy value of 98.67%, 98.67%, and 100%.