## 3 Results and Discussions
### Damage Classification Using the Deep Learning Approach
#### 3.3.1 Classification of AE Signals Based on the Damages Stage  
AlexNet, SqueezeNet, and the proposed model were built on Matlab (2022a) using the Deep Network Designer Toolbox by Mathworks. Several training modes were performed to make a CNN comparison and determine the best configuration. Table 4 shows the results of classifying the AE signal groups based on the damage stages. Four training modes were tested for the three CNN models (alexNet, SqueezeNet, and the new model). The first training mode, TM 1, used 200 image inputs, the sigmoid activation function, and average pooling for the pooling function. The results obtained are shown in Figure 16 for alexNet, SqueezeNet, and the new model, which are at 50% accuracy. An observation was made on the training time of each CNN: alexNet--56 min 28 s, squeezeNet--46 min 04 s, the new model--26 min 46 s. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **N\({}^{\circ}\) Training** \\ **Mode [Tm]** \\ \end{tabular} & \multirow{2}{*}{**Model**} & \begin{tabular}{c} **Number of** \\ **Inputs** \\ \end{tabular} & \begin{tabular}{c} **Poy of** \\ **Activation** \\ **Function** \\ \end{tabular} & \begin{tabular}{c} **Pooling** \\ **Avg/Max** \\ \end{tabular} & \begin{tabular}{c} **Validation** \\ **[\%]** \\ \end{tabular} & \
\begin{tabular}{c} **Testing [\%]** \\ \end{tabular} &
\begin{tabular}{c} **Times** \\ \end{tabular} \\ \hline \multirow{3}{*}{TM 1} & AlexNet & 200 & Sigmoid & Avg/Max & 50 & 50 & 56 min 28 s \\  & SqueezeNet & 200 & Sigmoid & Avg/Max & 50 & 50 & 46 min 04 s \\  & New Model & 200 & Sigmoid & Avg/Max & 50 & 50 & 26 min 46 s \\ \hline \multirow{3}{*}{TM 2} & AlexNet & 200 & ReLu & Avg/Max & 100 & 100 & 40 min 27 s \\  & SqueezeNet & 200 & ReLu & Avg/Max & 100 & 100 & 25 min 45 s \\  & New Model & 200 & ReLu & Avg/Max & 100 & 100 & 19 min 22 s \\ \hline \multirow{3}{*}{TM 3} & AlexNet & 1000 & Sigmoid & Avg/Max & 50 & 50 & 3 h 29 min \\  & SqueezeNet & 1000 & Sigmoid & Avg/Max & 48.7 & 50 & 2 h 57 min \\  & New Model & 1000 & Sigmoid & Avg/Max & 50 & 50 & 1 h 49 min \\ \hline \multirow{3}{*}{TM 4} & AlexNet & 1000 & ReLu & Avg/Max & 100 & 100 & 2 h 07 min \\  & SqueezeNet & 1000 & ReLu & Avg/Max & 100 & 100 & 1 h 16 min \\ \cline{1-1}  & New Model & 1000 & ReLu & Avg/Max & 100 & 100 & 36 min 32 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification of AE signals based on the damage stage. Figure 15: Categorization of AE signals based on similarities. For the second mode, TM 2, all parameters were kept the same as in the first training mode, except for the activation function, which was changed from Sigmoid to ReLu. The results are shown in Table 4 and Figure 17. By changing the activation function, the accuracy of all CNN models reached the maximum value of 100%. The difference in training time was observed for each model: AlexNet--40 min 27 s, squeezeNet--25 min 45 s, and the new model--19 min 22 s. In addition, the training time for each CNN model decreased by about 20% when the ReLu activation function was used. Figure 16: Classification based on the damage stage using TM 1: (a) AlexNet, (b) SqueezeNet, and (c) Proposed model CNN. For the third training mode, TM 3, the same parameters were used as for the first mode, but with a higher image input of 1000. Thus, 500 input images were used for each class (the elastic and plastic stages). The results shown in Table 4 show that AlexNet achieved 50% accuracy, SqueezeNet achieved 48.7%, and the new model achieved 50%. The development time increased with the amount of input data: 3 h 29 min for AlexNet, 2 h 57 min for SqueezeNet, and 1 h 46 min for the new model. The fourth training mode, TM 4, used 1000 images with the ReLu activation function. The results obtained are presented in Table 4, where 100% accuracy was achieved for all CNN models.