## 4 Al and ML in Bridge Inspection
### YOLO-Based Bridge Inspection Techniques
The results show that this model performed better than the other three conventional models (direct input of original images, sliding window cropping method, along with random centroid cropping process) tested. [PERSON] et al. [160] conducted a study in Hong Kong, one of the most populated cities globally that witnesses jam-packed traffic all year round. This makes automated bridge inspections more desirable. A modified version of YOLOv8 was proposed along with added features of a global attention module and Intersection over Union loss function to accomplish correct determination of cracks and also ameliorate the generalisation ability of the proposed model, which was named YOLOv8-GAM-Wise-IoU. The proposed model was highly economically effective because it maintained a modest size of 93.20M parameters and cut down the cost of training and inference processes. The model exhibited a high success rate in maintaining the safety and integration of reinforced concrete bridges in the Hong Kong environment. [PERSON] et al. [161] identified cracks in concrete using YOLOv5-TS (this initial model was deployed in combination with SPPCSPC) to achieve better adaptive image output and extract multiple-size receptive fields. Four types of mechanical cracks (based on crack inclination) were distinguished, viz., horizontal cracks (0\({}^{\circ}\)-20\({}^{\circ}\)), low-angle cracks (20\({}^{\circ}\)-45\({}^{\circ}\)), vertical cracks (70\({}^{\circ}\)-90\({}^{\circ}\)), and high-angle cracks (45\({}^{\circ}\)-70\({}^{\circ}\)). An applicable and practical model was the end product of the study, which was YOLOv5-TS with the following output features: [EMAIL_ADDRESS] (0.752), [EMAIL_ADDRESS] (0.518), and recall (0.794). Experiments were executed on the ZJU SYG crack dataset. Twin benefits were achieved using transposed convolution-enhanced network capacity that enabled it to self-learn weights and minimise characteristic information loss. According to [PERSON] et al. [163], cracks on bridge surfaces can severely hamper their safety and serviceability properties. Typically, available manual bridge inspection methods are restricted because of the severe workload of data processing and are also highly dependent on the subjective judgement of the inspecting professionals. This study was based on crack segmentation methods that involved U-Net, Gabor filters, and convolutional block attention modules. Hence, both spatial and frequency domains were focused so that ameliorated crack detection could be obtained and the impact of image disturbances could be reduced. The authors created a multi-source annotated crack dataset to train and evaluate their proposed model. The entire setup was designed to expose the model to different types of crack appearances and complex backgrounds that honed the model's generalisation capacity. A few important results achieved by the model are an IoU of 60.62% and an F1 score of 74.49%, transcending benchmark segmentation networks. The model allows cross-domain feature fusion and multi-source data utilisation in crack segmentation in situations with multiple patterns and diverse backgrounds.