## 5 Conclusions  
This research explored the application of machine learning (ML) classification techniques to classify damaged plates based on their failure location and size, e.g., R-Classification and S-Classification tasks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Algorithm** & **Validation Accuracy (\%)** & **Testing Accuracy (\%)** \\ \hline Ensemble-Subspace KNN & 94.65 & 98.15 \\ Ensemble-Bagged Trees & 94.2 & 98.15 \\ KNN-Fine KNN & 93.6 & 96.3 \\ Ensemble-Boosted Trees & 93.4 & 98.15 \\ Kernel-SVM Kernel & 92.8 & 98.15 \\ Linear Discriminant & 92.4 & 94.4 \\ SVM-Quadratic SVM & 92.2 & 94.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of the S-Classification procedure with different algorithms. Figure 18: S-Classification: confusion matrices for (**a**) training and (**b**) testing steps. In the initial phase of the experiment, a composite plate measuring 300 mm \(\times\) 300 mm was used, with a cuboid of 20 mm \(\times\) 25 mm placed on its surface to simulate various reversible damage configurations. Guided waves were propagated over the plate through a central piezoelectric sensor, and the resulting diagnostic waves were captured by four sensors placed in diametrically opposed directions. The plate was divided into four equal regions, and the experiment was repeated 40 times for each region, moving the damage position in each of the plate's four zones. After collecting 160 signals, high-frequency noises were filtered out using the Butterworth filter, and 30 features were extracted in the time, frequency, and time-frequency domains. The 120 extracted features were designated as the primary feature vector for each experiment. To examine the ability to localize damage using only two sensors, 9 features were selected for the combination of sensors 2 and 3, and 12 features were selected for the combination of sensors 4 and 5 using the sequential forward selection procedure. Several ML classifiers were then used to classify the labeled signals using MATLAB(r) Classification Learner App. As the primary accuracy was not satisfactory, an optimization process was employed to find optimized hyperparameters. The final accuracy in the training and testing phases was 96.32% and 95.83%, respectively, for the selected features from sensors 2 and 3. Meanwhile, the training accuracy remained consistent at 96.32%, but the testing accuracy decreased to 91.6% for the combination of sensors 4 and 5. In the second section and to intelligently detect the damage size, three cuboids of 20 mm \(\times\) 25 mm, 20 mm \(\times\) 35 mm, and 20 mm \(\times\) 45 mm were used to experimentally simulate various damage scenarios on the same plate as the previous part. For each damage size, the test was repeated 180 times, changing their positions across the plate. Once the 540 signals were collected by the four sensors, the same procedure involving noise reduction, feature extraction, and feature selection was employed with the difference that the data from all four receiving sensors is used in the feature extraction. In the classification stage the 13 selected features were fed into the existing classifiers models, the Ensemble-Subspace KNN emerges as the best performer, scoring 94.7% and 98.1% accuracy in the validation and testing phases, respectively. The findings demonstrated the potential of this technique for the damage diagnosis in composite plate-like structures, whereas only two receivers can be employed for the automatic sake of localization and severity without losing accuracy. Implementing the proposed methodology may present certain challenges, particularly due to potential uncertainties arising from varying environmental conditions. This is because piezoelectric sensors, which are integral to the methodology, are highly sensitive to temperature changes. This sensitivity could lead to shifts in the distribution of classes between the training (source) and testing (target) domains.