## 4 Results and Discussion
### Model Evaluation and Comparison  
The constructed test set was fed into the trained network model to obtain the confusion matrices for the location classification task and the energy prediction task, as shown in Figure 12. For the damage location classification task, because the confusion matrix for damage location classification had elements of 0 at all positions except the diagonal, all damage locations of the 240 damage samples in the test set were correctly predicted with 100% accuracy for both single-task learning and multi-task learning. All damage locations of the 240 damage samples in the test set were correctly predicted for both single-task learning and multi-task learning. From this, it can be calculated that the accuracy of the damage location classification task was 100% in both cases. In the impact energy classification task, single-task learning incorrectly predicted eight samples with 96.67% accuracy. Multi-task learning incorrectly predicted only 4 samples, and the remaining 236 samples were all correctly predicted with an accuracy of 98.33%. It can be seen that the accuracy of multi-task learning in predicting the magnitude of impact energy was significantly higher than that of single-task learning. Precision, recall, and F1-score were used to evaluate the damage classification results. For both single-task learning and multi-task learning, the accuracy of the test set in the damage location classification task was 100% and hence, the precision, recall, and F1-score were also 100%. Table 3 shows the accuracy, recall, and F1 score for the damage energy classification task for each energy damage. From the table, it can be seen that all the indexes of multi-task learning were stronger than single-task learning, which indicated that multi-task learning can closely link multiple tasks, and the features between multiple tasks complemented each other, which ultimately improves the prediction accuracy and performance of the network. Therefore, the proposed means of simultaneous impact damage location prediction and impact damage energy prediction is feasible. Figure 11: Visual effect of feature dimensionality reduction via T-SNE. (**a**) Location classification task before training; (**b**) location classification task after training; (**c**) energy classification task before training; (**d**) energy classification task after training. In practice, single-task learning consumes a lot of hardware and software resources, resulting in slow computation. Therefore, we compared the training times of multi-task learning and single-task learning, as shown in Table 4. Compared with single-task learning, multi-task learning took less time to train and computed faster, reducing the total training time by 30.83%. Figure 12: Confusion matrix. (**a**) Damage location confusion matrix for single-task learning; (**b**) damage energy confusion matrix for single-task learning; (**c**) damage location confusion matrix for single-task learning; (**d**) damage energy confusion matrix for single-task learning. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Energy** & **Precision (\%)** & **Recall (\%)** & **F1-score (\%)** \\ \hline Single-task learning & 0.5 ] & 95.74 & 98.90 & 97.29 \\  & 1.0 ] & 97.05 & 92.96 & 94.96 \\  & 1.5 ] & 97.43 & 97.44 & 97.43 \\  & 0.5 ] & 95.79 & 100 & 97.85 \\  & 1.0 ] & 100 & 95.77 & 97.84 \\  & 1.5 ] & 100 & 98.71 & 99.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance indicators of impact energy classification task. To verify the validity of the proposed model for impact damage location prediction and impact energy prediction, it was compared with other commonly used machine learning algorithms in pattern recognition. Nine commonly used empirical characteristic indexes were extracted from signals [35]. Empirical features were input into support vector machines (SVMs), decision trees (DTs), and random forest (RF) model training and testing.