## 4 Al and ML in Bridge Inspection
### Neural Network-Based Bridge Inspection Techniques  
Table 3 gives a summary of recent research on CNN- and ANN-based bridge inspection. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & **Objective** & **Technique** & **Dataset** & **Outcome** & **Feature/Limitations** \\ \hline \multirow{4}{*}{[127]} & \multirow{4}{*}{To develop a crack detection model using TL.} & Image augmentation was achieved using rotation, offset, flip, film, clip, and zoom. CNN and VGG16 & A total of 167 RGB images of 1024 \(\times\) 768 pixels. & Enhanced accuracy and robustness, along with a stupendous F1 score. & \(5\%\) enhancement in accuracy. \\ \cline{2-6}  & & & & & Model was trained on a \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \hline \multirow{4}{*}{[128]} & \multirow{4}{*}{Reviewed ML-based algorithms used for the SHM of buildings.} & \multirow{4}{*}{ANN, CNN, and SVM} & \multirow{4}{*}{NA} & Data processing achieved by the Hilbert–Huang transform or the wavelet packet transform yielded good results. & Emphasis was placed only on using fog devices to minimize noise filtering and enhance data compression and data fusion after seismic events. \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \hline \multirow{4}{*}{[129]} & \multirow{4}{*}{To investigate the link between enhanced computational intelligence and SHM solutions.} & \multirow{4}{*}{ANN, Hybrid ANN-GA} & \multirow{4}{*}{NA} & The results were better than those of faster & Besides cracks, other types of damage, like spalling and exposed reinforcement, & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \hline \multirow{4}{*}{[130]} & \multirow{4}{*}{To Improve the mAP.} & \multirow{4}{*}{R-CNN} & \multirow{4}{*}{Canon EOS SDR name camera captured 1000 digital images of 8688 \(\times\) 5792 pixels} & The results were better than those of faster & Besides cracks, other types of damage, like spalling and exposed reinforcement, & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \hline \multirow{4}{*}{[130]} & \multirow{4}{*}{To detect sub-surface damage in bridges using NDT technology and GPR.} & \multirow{4}{*}{CNN} & The testing dataset featured 2898 GPR signals. & A high accuracy of around 84\% was obtained with the proposed model. & The dependability of the 1D-CNN is dependent on the quality and uniformity of GPR data. Disparities in data gathering techniques, with the same amount of samples. & The top-level data gathering techniques, and \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \hline \multirow{4}{*}{[131]} & \multirow{4}{*}{To explore several ML techniques in structural design, construction quality management, bridge engineering, and reinforced concrete bridge inspection.} & \multirow{4}{*}{ANN} & \multirow{4}{*}{NA} & ANN is best suited for classification and regression tasks. & Reinforced concrete bridges were explored in microscopic detail; \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Description of recent studies based on neural networks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
[PERSON] et al. [127] conducted a comparative study for the preliminary detection of fatigue cracks (three types) by inputting relevant data into a shallow CNN and VGG16. [PERSON] and [PERSON] [128] explored CNN, ANN, and SVM techniques and compared them for their applicability to concrete bridge crack detection. The basis of the proposed model is the following: