## 4 Al and ML in Bridge Inspection
### Neural Network-Based Bridge Inspection Techniques
A study conducted by [PERSON] et al. [145] aimed towards the crack detection of concrete bridges across the USA with the help of a combination model consisting of a visual transformer (ViT) having image enhancement detector algorithms. Traditional crack detection techniques frequently prove inadequate due to their substantial dependence on time and resources for implementation. Thus, the proposed model was a solution because combining ViT with diverse image enhancer techniques led to better accuracy when detecting cracks in concrete bridges. The CNN model achieved around 99% accuracy, and the proposed model was per the Industry 4.0 rubric; it also exhibited automatic manual inspection at low cost, allowing accurate and timely crack detection. Negative results can be obtained for normal concrete if extra sophisticated specific tests are applied for crack detection. Therefore, it is important to determine what type of test should be applied under what occurrence and type of concrete. These innovations ensure safety through dependable detection and prompt maintenance while aligning with Industry 4.0 goals by automating manual inspections, lowering costs, and promoting technological integration in public infrastructure management. [PERSON] et al. [146] conducted a study using unmanned aircraft images and a lightweight deep CNN to detect concrete bridge damage to achieve pixel-level crack segmentation. Along with the conventional encoder-decoder unit architecture, an hourglass-shaped depthwise separable convolution structure was used to reduce the model parameters. This was followed by introducing a lightweight channel attention module to enhance the model's fuzzy ability and segmentation accuracy. Local cross-channel interaction could be achieved irrespective of dimensionality reduction, and there was a significant improvement in the segmentation performance of the model because it was based on lightweight and efficient attention modules. According to [PERSON] et al. [147], many methods based on square kernel struggle to view and capture crack features efficiently; they also fail to reveal long-range probability, and background noises become a limiting feature because of their restricted suppression ability. All of these elements lead to reduced efficiency of bridge crack detection. To overcome this problem, a multi-stage feature aggregation and structure awareness network (MFSA-NeT) was proposed to detect concrete bridge cracks at the pixel-level. In the coding stage, square and strip convolutions were combined to perceive a linear picture of cracks in the concrete. Square convolution helped gather microscopic-level information. In contrast, strip convolution interacted with local features to maintain the long-range dependent relationship between various crack areas. Strip convolution also achieves suppression of the immediate background around the crack region. Sharpened edges of the concrete bridge crack were achieved using a feature attention fusion block that allowed for fusing features from the encoder and decoder at the same stage. To fully utilise shallow detail features and deep semantic features, the mix-up of features obtained from different stages was conducted to extract fine-grained segmentation outcomes. [PERSON] et al. [151] developed a method for crack detection in concrete structures. The method was based on TensorFlow CNN and image processing tools, and thousands of cracked and non-cracked images obtained from the structure surface dataset were used to develop the model. Training efficiency was enhanced by extracting image features in the pre-processing stage.