## 4 Al and ML in Bridge Inspection
### Neural Network-Based Bridge Inspection Techniques
The combination approach delivered enhanced damage detection and accuracy. Cracks on the surface were identified using a shallow CNN proposed by [PERSON] et al. [136]. Comparative studies were conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results reported show that the proposed method performs better and can find concrete cracks in realistic situations like strong light, spots, and shadows. Image processing techniques can be obstructed in real-world situations, and this study proves a practical and applicable solution to these by detecting extremely minuscule cracks up to 1.5 mm. [PERSON] et al. [137] researched different damages, including cracks on reinforced concrete bridges, to analyse their safety levels, plan maintenance, and understand service-ability capacity. The image classification process detected five defects, including cracks, using a combination of CNN's Xception and Vanilla models. [PERSON] et al. [139] conducted a study to detect cracks at the bridges' bottom portion using faster R-CNN and building information modelling (BIM) techniques. Crack images correlate with a BIM model designed for the selected bridge and the box girder family exhibiting cracks, along with three categories of cracks: transverse, longitudinal, and turtle. This unique combination of bridge crack detection, consisting of UAVs and modelling techniques, helped detect remote cracks, which are otherwise difficult to detect in manual inspection, and thus has practical applicability in civil engineering. [PERSON] et al. [140] carried out drone photography of concrete cracks and evaluated the gathered information using ML tools like CNN; also, a model called the CenterNet model based on CNN was proposed. In the first step, the conventional CNN model introduced a channel-space attention mechanism, imparting the ability to analyse the image closely. After that, a feature selection module was incorporated to scale the feature map. This module served a dual purpose; in the downsampling stage, it combines images of similar sizes in the channel dimension. On the other hand, in the upsampling stage, this module figures out combined features and allows their fusion with the output features of upsampling. In the final stage, target size loss is balanced through a smooth L1 loss to IoU loss. This is carried out to diminish its inefficacy in adapting according to target size. Outcomes from this study showcased a reduction in FPS by 123.7 Hz, enhancement in GPU memory by 62 MB, increment in FLOPs by 3.81 times/sec, and increase in the AP by 15.4% when viewed in contrast to the primordial (original) model. The collateral advantage of CenterNet was that real-time performance and robustness were attained while GPU memory stability was maintained throughout the entire operation, making the model practicable. [PERSON] et al. [141] undertook safe and practical experimentation to detect surface cracks in concrete structures, allowing real-time assessment with the help of a combination deep neural network and an autonomous UAV. The study used Fast Region-based CNN (FRCNN) and Residual Network (ResNet), which permitted the real-time and accurate detection of cracks through the ongoing live-video feeds. The study was based on real videos transmitted by the UAV to a computer platform equipped with tools for spotting surface cracks. About 1000 images from a diverse dataset were used to train the FRCNN-ResNet model. The reported precession rate is 93.9% with a 59.7 ms inference time. The model allows for improved safety and cost savings of civil infrastructures made of concrete, including bridges. [PERSON] and [PERSON] [144] conducted a crack detection study on bridges, roads, and concrete walls. A combination of vision transformers and CNN was used to diagnose cracks in these three structures automatically. The performance of the combination model was improved through various strategies like transfer learning, optimised hyperparameters, and data augmentation.