## 4 Al and ML in Bridge Inspection
### Neural Network-Based Bridge Inspection Techniques
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Objective** & **Technique** & **Dataset** & **Outcome** & **Feature/Limitations** \\ \hline \multirow{4}{*}{[141]} & Assessing cracks on concrete surfaces using UAVs. & A combination version of FRCNN and ResNet model & 1000 images of 640 \(\times\) 480 pixels from diverse datasets were used. & The proposed combination model gave a 93.3\% precision rate and a quick inference time of 59.7 ms. & Unremitting real-time crack, especially, was possible within a reasonable budget. The investigation may inadequately assess the model’s capacity to single across diverse concrete constructions with varying textures, colours, and compositions. & Crack detection in millimetres rather than 99.2\% for the pixels is more accurate and practically applicable. & Crack detection in millimetres rather than 99.1\% for the laser calibration to measure the crack in millimetres. \\ \hline \multirow{4}{*}{[142]} & Crack detection in structures including bridges. & CNN ViT & SDNET2018 [120] dataset. & About 92.81\% accuracy was achieved in bridge crack detection. & A combination model was developed using vision transformers and CNN. The model’s effectiveness in detecting very fine or hairline cracks is not thoroughly evaluated. & A combination model was trained using vision transformers and CNN. \\ \hline \multirow{4}{*}{[145]} & \multirow{4}{*}{Crack detection in concrete bridges.} & \multirow{4}{*}{CNN} & A total of 200 images of 227 \(\times\) 227 pixels in each category of cracked and non-cracked dataset. & A 99\% accuracy was required with moderate expenditure. & The model’s performance across varied \\ \hline \multirow{4}{*}{[146]} & \multirow{4}{*}{Crack detection in concrete bridges.} & \multirow{4}{*}{Crack detection in CNN and unmanned aircraft system (UAS)} & A total of 6000 images with image resolution of 200 \(\times\) 200 pixels were used for training, validation and testing in 72:21 ratio. & The model exhibited 97.42\% accuracy, 97.62\% precision, and 97.23\% recall and attained 93.25\% IOU. & The technical changes made the proposed model & the proposed model & The model exhibited 97.42\% accuracy, 97.62\% securing the dimensionality element. & The model exhibited 97.23\% recall and attained 93.25\% IOU. & The technical changes made the proposed model & the proposed model & The model exhibited 97.42\% accuracy, 97.62\% securing the dimensionality element. & The model’s performance across varied \\ \cline{1-1} \cline{2-2}  & \multirow{4}{*}{Crack detection in concrete bridge.} & \multirow{4}{*}{Crack detection in Square/strip convolution} & \multirow{4}{*}{Squarre/strip Crack1S315 [49], CFD [150],} & \multirow{4}{*}{BluredCrack [148], CFD [150],} & \multirow{4}{*}{Precision: 73.74\%, recall: 77.04\%, F1: 75.30\%, IoU: 60.48\%.} & \multirow{4}{*}{Precision: 73.74\%, background noises. The study does not elaborate on how MFA-Net’s outputs can be integrated into broader structural health monitoring frameworks.} & Effective and accurate crack identification was possible. The study fail to perceive long-range dependencies between crack regions, and has weak suppression noisy for background noises, leading to low detection precision of bridge cracks. \\ \hline \multirow{4}{*}{[151]} & Crack detection on the surface of concrete infrastructure. & \multirow{4}{*}{TensorFlow CNN and image processing} & \multirow{4}{*}{6000 images of 227 \(\times\) 227 pixels.} & \multirow{4}{*}{The proposed model exhibited an accuracy of 97.11\% and 97\% F1 score.} & Effective and accurate crack identification was possible. The study fail to perceive long-range dependencies between crack regions, and has weak suppression noisy, leading to low detection precision of bridge cracks. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(Cont.\)comparison was input data, type of feature selection applied, data size, stage of damage under consideration, and achieved accuracy of the deployed ML model.