# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 6 Evaluation of 1-D CNN Models for Untrained Damage Cases  
### Damage Classification by 1-D CNN Model Using Time-History Responses  
Three untrained damage cases were established, as outlined in Table 3. To create partially untrained data scenarios, certain damage levels were excluded from the training and validation datasets. For Case 1, the damage levels D2, D4, and D6 were excluded from among the eight damage levels (D0-D7), resulting in 3060 responses remaining in the training and validation datasets. For Case 2, the training and validation datasets excluded four damage levels (D1, D3, D5, and D7). Case 3 excluded data from damage levels D1, D2, D5, and D6, leaving 2448 responses in the training and validation datasets.  
Figure 22 shows the loss values of the CNN models using partially untrained time-history databanks. For all three cases, the training and validation losses decreased sharply in a few initial iterations and converged by the end of the 40 iterations. The training and validation losses demonstrated good performance of all three CNN models.  
Figure 23 shows the damage classification results of the CNN model using partially untrained time-history databanks. The red ticks indicate the correct level of the inflicted damage. With untrained cases, the CNN model was limited in accessibility to certain damage levels. When encountering an unseen damage level, the CNN model could not directly classify data to untrained levels. Instead, it was expected to return the classification results in the most similar trained damage levels (the green ticks).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**Data Type**} \\ \cline{3-4}  & & **Training \& Validation Datasets** & **Evaluation Dataset** \\ \hline
1 & Excluding damage levels D2, D4, and D6 & 3060 & \\
2 & Excluding damage levels D1, D3, D5, and D7 & 2448 & 1224 \\
3 & Excluding damage levels D1, D2, D5, and D6 & 2448 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Untrained damage cases for evaluating the 1-D CNN model.  
Figure 22: Loss values of the 1-D CNN model using partially untrained time-history databanks: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3.  
Figure 23a shows the damage classification results for the untrained Case 1. The data in all trained damage levels wasaccurate, with no misclassifications. The untrained damage levels were classified to nearby ones: D2 was classified to D3; D4 was classified to D3 and D5; D6 was classified to D5 and D7. Figure 23b shows the damage classification results for the untrained Case 2. There was no misclassification in the classification results for trained levels (i.e., D0, D2, D4, and D6). The untrained damage levels were classified into nearby ones: data from D1 and D3 were classified as D2; and data from D5 and D7 were classified as D6. Figure 23c shows the untrained Case 3. The distance between trained damage levels increased to two levels when D1, D2, D5, and D6 were neglected. The CNN model maintained its ability to make correct classifications for trained damage levels. The untrained damage levels D1 and D2 were classified as the nearest damage level D3, while untrained damage levels D5 and D6 were classified as the nearest damage level D7.  
Figure 23: Damage classification results of the 1-D CNN model using partially untrained time-history databank: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3. The red ticks indicate the correct level of the inflicted damage. When the CNN model could not directly classify data to untrained levels, it returned the classification results in the most similar trained damage levels (green ticks).  
In practice, only a finite number of damage levels can be provided for model training. The results with partially untrained databank demonstrated the CNN model's ability to classify unseen damage levels into nearly similar damage levels. This reduced the need to provide many training cases, making the CNN model feasible for real applications in caisson systems.