## 4 Application to New Datasets  
### Dataset for Application Test  
One thousand datasets for random excitation and traffic loading were generated to evaluate the proposed model for new datasets that were different from the trained dataset in that sampling time was extended from 60 to 120 to 150 s, and damage severity was randomly selected to be 0% and 20-50%. Additionally, the number and velocity of vehicles were increased. Table 7 compares the trained  
According to the test results shown in Tables 8 and 9, the classification accuracy for random excitation was perfect and 1.0% higher than for traffic loading. Most of the misclassifications occurred in determining the existence of damage in the traffic loading case, whereas the random excitation case shows perfect classification result. These results demonstrate that the system vibrates more when random excitation is used than when traffic loading is used; thus, random excitation is used for assessing the status. However, considering the application to real structures that are tested with ambient vibration, the test result of traffic loading shows the robustness of the proposed method with a high accuracy of 99.00%.  
Tables 10 and 11 summarize the confusion matrix of each case that represents the classification capability of whether the system is intact or damaged. For analysis, \"intact\" is considered positive and \"damage\" is considered negative. From the confusion matrix, recall and fallout, represented by the true positive rate (TPR) and false positive rate (FPR), respectively, can be analyzed. TPR represents the ratio of the number predicted as positive cases to the number of actual positive cases and can be expressed  
\[TPR=\ \frac{TP}{TP+FN} \tag{9}\]  
FPR represents the ratio of those predicted as positive cases to the number of actual negative cases and is expressed as  
\[FPR=\ \frac{FP}{TN+FP} \tag{10}\]  
In both random excitation and traffic loading cases, the trained model showed that the TPR was 100% and had excellent capability to categorize \"intact.\" On the other hand, the capability to categorize damage represented by FPR was 0% for random excitation and 0.65% for traffic loading. These results show that the trained model performs well for classification of whether the condition of the structure is intact or damaged.  
To show the performance of trained model, visualization using t-distributed stochastic neighbor embedding (t-SNE) [37] was conducted. The t-SNE is a nonlinear dimensionality reduction technique used to visualize high-dimensional data in low-dimensional space. This technique was used to create 2-D distribution map of the predicted location of damage and intact with the output from GAP layer, which was right before the classification layer.  
Two 2-D maps, random and traffic loading, are shown in Figure 7, and each map shows 1000 points from test datasets. Figure 7 shows that points in the same category are close to each other and form clusters. Clustering was observed more clearly for random excitation result than for traffic loading.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual**} \\ \cline{3-4}  & & **Intact** & **Damaged** \\ \hline \multirow{2}{*}{Predicted} & Intact & TP (88) & FP (0) \\  & Damaged & FN (0) & TN (912) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Confusion matrix of random excitation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual**} \\ \cline{3-4}  & & **Intact** & **Damaged** \\ \hline \multirow{2}{*}{Predicted} & Intact & TP (81) & FP (6) \\  & Damaged & FN (0) & TN (913) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Confusion matrix of traffic loading.