# A Hybrid Learning Framework for Enhancing Bridge Damage Prediction
## 3 Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Refrences**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**K-Fold CV**} & \multirow{2}{*}{**No. of Epochs**} & \multicolumn{3}{c}{**Datasets**} \\ \cline{6-7}  & & & **BCD** & **Bridge** & **DIMEC-Crack** \\ \hline \multirow{6}{*}{[PERSON] et al. [15]} & VGG16 & & & 99.83 & - & - \\  & VGG19 & & & 99.67 & - & - \\  & Xception & & & 99.67 & - & - \\  & ResNet 50 & & & 99.67 & - & - \\  & ResNet 101 & & & 99.5 & - & - \\  & ResNet 152 & - & & 99.83 & - & - \\  & InceptionV3 & & & 99.83 & - & - \\  & InceptionResNet & & & 99.5 & - & - \\  & V2 & & & 99.83 & - & - \\  & MobileNetV2 & & & 99.83 & - & - \\  & DenseNet121 & & & 99.83 & - & - \\  & EfficientNetB0 & & & 99.83 & - & - \\ \hline [PERSON] et al. [16] & TL model & - & 20 & 99.72 & - & - \\ \hline [PERSON] et al. [18] & TL model & 5-fold & 10 & - & 95.89 & - \\ \hline [PERSON] et al. [17] & \
\begin{tabular}{c} HOG + ULBP + \\ KPCA+ SVM \\ \end{tabular} & 5-fold & - & - & 99.29 & - \\ \hline \multirow{6}{*}{[PERSON] et al. [19]} & Atrous & & & & \\  &
\begin{tabular}{c} convolution, \\ ASPP, and \\ depthwise \\ separable \\ convolution \\ \end{tabular} & - & 300 & 96.37 & - & - \\ \hline \multicolumn{2}{c}{MobileNetV3\_Large} & 10-fold & 20 & 82.14 & 72.59 & 91.51 \\ \hline \multirow{2}{*}{Proposed method} & LBP-BoVW + & \multirow{2}{*}{10-fold} & \multirow{2}{*}{20} & \multirow{2}{*}{**99.99**} & \multirow{2}{*}{**99.97**} & \multirow{2}{*}{**100**} \\  & Appriori + MobileNetV3\_Large & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The classification accuracy score comparison. [PERSON] et al. [15] employed 12 transfer learning (TL) methods: VGG16 achieved an accuracy of 99.83%, with more than 99% for each precision, recall, and F1-score; VGG19 achieved an accuracy of 99.67%, with a precision, recall, and F1-score of \(>\)99%; Xception achieved 99.67% and more than 99% for each precision, recall, and F1-score; ResNet 50 achieved an accuracy of 99.67%, with the precision, recall, and F1-score reaching \(>\)99%; ResNet 101 achieved an accuracy of 99.5%, a precision of 100%, a recall of 99%, and a recall of 99%, respectively. Figure 3: The accuracy and loss plot of the best results for (**a**) the BCD dataset with a feature size of 100, (**b**) the Bridge dataset with a feature of size 300, and (**c**) the DIMEC-Crack dataset with a feature size of 100.  
and an F1-score of 99.5%; ResNet 152 gained an accuracy of 99.83% and >99% for each precision, recall, and F1-score; InceptionV3 achieved an accuracy of 99.83%, a precision of 99%, a recall of 99%, and an f1-score of 99%; InceptionResNet V2 achieved an accuracy of 99.5%, with a precision of 100%, a recall of 99%, and an F1 score reaching 99.5%; MobileNet achieved an accuracy of 99.83%, with more than 99% for each precision, recall, and F1-score; MobileNetV2 achieved an accuracy of 99.83%, with a precision, recall, and F1 score of >99%; DenseNet 121 achieved an accuracy of 99.67%, with a precision, recall, and F1-score of 100%; and the EfficientNetB0 model yielded an accuracy of 99.6% and a precision of 99.83%, with a precision, recall, and F1-score of >99%. Figure 4: The confusion matrix of the best results for (**a**) the BCD dataset with a feature size of 100, (**b**) the Bridge dataset with a feature size of 300, and (**c**) the DIMEC-Crack dataset with a feature size of 100. [PERSON] et al. [16] also used transfer learning, utilizing 13 layers of VGG16 and 2 FC layers. They trained their proposal with BCD and CCIC datasets. For the BCD dataset, they achieved an accuracy of 99.72%, a precision of 96.46%, and an AUC of 99.99%. In their study, [PERSON] et al. [17] used ULBP, HOG, KPCA, and SVM to classify concrete cracks in the self-made bridge dataset. They achieved an accuracy of 99.26%, a precision of 95%, a recall of 99.23%, and an F1 score of 99.12%. [PERSON], with another team [18], utilized the Bridge dataset, supplemented it with culvert images, and applied transfer learning to the CNN models.