## 4 Results and Discussion  
From the machine learning setup, it was possible to accomplish the required simulations and in Figure 13 it is possible to see the loss function for both the training and validation cycles. In this figure, there is a gradual decrease in loss from both cases, with the training loss always lower than the validation. It is possible to see that, after around 500 epochs, there is a stabilization of the value of the validation loss, followed by an increase at around 800 epochs. For the training loss, there is a slow decrease after 500 epochs, thus showing signs of overfitting. The option for 1500 epochs was for allowing to visualize the behavior of the loss curve and have the guarantee of its convergence.  
In Figure 14, the predicted values obtained from the validation dataset were compared to the original values and the error was plotted from smallest to largest. In the figure, the difference between them is plotted, showing that there are more cases with less than 50 MPa of error, with a few cases above 150 MPa of error. These values by themselves do not hold a large significance but, when compared with the values of Young's modules from weak adhesion, it is possible to see that most of the error represents, on average, less than 5% of the damage. This means that this method can detect the level of damage with a 95% accuracy without needing to know where the damage is located. Furthermore, the figure shows that only a few occurrences have an error over 25%, which may be acceptable. However, due to the possible future practical applications of this method, it is  
Figure 12: Graphic representation of a FFNN used in the final study with six dense layers.  
Figure 13: Plot of the loss function for both the training and the validation sets per epoch.  
important to better understand when these errors occur, to allow for further investigation and increase robustness.  
To analyze the data even further, the cases were separated between their real value, and in Figure 15 it is possible to see how the error is spread out for each case. It can be seen that the case with the most and largest errors is when the weak adhesion has a Young's modulus of 100 MPa, while all other cases present few errors with a value over 100 MPa. This shows that there is a more pronounced difference between the simulations in the lower range tested and thus, to create a more robust method, a minimum threshold should be adopted, where any values lower than 200 MPa are indicative of damage and no further evaluation should be taken.  
Following the previous analysis, the data were divided into not only the error but also the overall position of the error for each different case of real value. This division can be seen in Figure 16, where each of the circles corresponds to a different level of real value and the overall dimension of the circles represents the size of the error. In this Figure, it is possible to see that the larger errors occur on the edges of the panels where there is less information being captured by the sensors and the information that does get captured can be subjected to reflections on the walls. This shows not only where the errors are occurring but also reinforces that the previous study of comparing the simulated and experimental data was done in sub-optimal conditions and still generated significant positive results.  
Figure 14: Plot of the number of cases in each error class showing that most errors are below 50 MPa.  
Figure 15: Comparison between the error for each real value of damage.