## 3 Experiments
### Experimental Results  
The proposed model was trained on a server equipped with an Intel Xeon Silver 420 (10) @ 2.194 GHz CPU and an Nvidia Tesla V100 (32 GB) GPU. The model was developed using the Python (version 3.7.13) programming language with the Python modules Keras (version 2.2.4) and Pycaret (version 2.3.10). We used Keras to build the 1D-CNN model and Pycaret to build the SVM model.  
We adopted five-fold cross-validation to train and validate the models. The experimental results of the proposed method compared with other baseline methods are given in Table 3. From Table 3, we can see that the model performance of only using SVM combined with four-layer wavelet packet decomposition was the worst, with an average accuracy of 75.7%. This might be because the complexity of the problem to detect the damage locations and damage severity simultaneously exceeds/ed the learning ability of the model, and the informative features for classification were not well extracted. We can also find that the  
\begin{table}
\begin{tabular}{c c l l c} \hline \hline
**Layer** & **Output Shape** & **Parameter** & **Activation** & **Variables** \\ \hline Input & 1024 \(\times\) 8 & None & None & 0 \\ Convolution 1-D & 1021 \(\times\) 8 & Kernel number: 4; Kernel size: 8 \(\times\) 8; & ReLU & 264 \\ Convolution 1-D & 1014 \(\times\) 16 & Kernel number: 8; Kernel size: 16 \(\times\) 8; & ReLU & 1040 \\ Max Pooling 1-D & 507 \(\times\) 16 & Kernel number: 2; & None & 0 \\ Convolution 1-D & 500 \(\times\) 16 & Kernel number: 8; Kernel size: 16 \(\times\) 8; & ReLU & 2064 \\ Global Average & 16 & None & None & 0 \\ Pooling 1-D & 16 & None & None & 0 \\ Dropout & 16 & None & None & 0 \\ Dense & 7 & None & Softmax & 119 \\ Total parameters & & & 3487 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of the 1D-CNN used in this paper.  
method using 1D-CNN worked better than the method using only SVM, which verifies the powerful feature extraction ability of 1D-CNN in this task.  
Moreover, the two-stage approach 1D-CNN and 1D-CNN worked better than the other directly identification approach, which verifies the effectiveness of the idea of \"divide-and-conquer\". In addition, the method using 1D-CNN and SVM achieved the best results, slightly higher than that of 1D-CNN and 1D-CNN. The specific experimental results of the two-stage methods are shown in Table 4. Since the 1D-CNN was used to identify the damage locations in the first stage in both two methods, their accuracies of detecting damage locations were very close, while in the second stage, the SVM was able to maintain a stable accuracy of 100%, which is better than that of 1D-CNN, indicating that SVM can achieve better results on the case of the small number of samples. In addition, deep learning-based methods need sufficient training samples; otherwise, they fall into an overfitting state and lower the generalization performance of the model.  
Figure 6a,b show the confusion matrix of the 1D-CNN and 1D-CNN&SVM methods. It can be seen that when only 1D-CNN was used to identify the damage location and damage severity, the error was mainly concentrated on the case of different damage severity at the same damage location, such as three samples of D1 were identified as D2 and eight samples of D5 were identified as D4. The remaining six D9 samples were incorrectly identified as D6 (note that D9 represents the existence of damage in the fifth and seventh layers, and D6 represents the existence of damage in the seventh layer only), and the model only identified one of the damage locations. In contrast, in the case of using 1D-CNN and SVM, there were no erroneous samples within the confusion matrix, and the two-stage method based on 1D-CNN and SVM improved the performance of the model.