# Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification
## 6 Results and Discussion
### Dcamination Identification Results  
The delamination identification results obtained by using PSO aided by the DL-based surrogate model are presented in Figure 10. Several runs were performed due to the metaheuristic nature of the PSO algorithm, and the results were selected for two runs for illustration purposes. The following cases were selected, namely case 1 (Figure 10a,b), case 2 (Figure 10c,d), and case 3 (Figure 10e,f), where the damage identification difficulty  
Figure 9: Third scenario: comparison of predicted frames with the label frames at 10 th, 20 th, and 30 th frames after the interaction with delamination.  
can be ranked from highest to lowest. The most difficult case was case 1, in which the delamination was in the corner of the plate. The delamination for case 2 was located very close to the top edge of the plate, where edge reflections can overshadow reflections from delamination. The delamination for case 3 was quite large and far away from the plate's edges, so it should have been easily detected.  
Actually, the damage identification difficulty level is reflected in the obtained IoU values, which are shown in the zoomed-in regions around the delamination in Figure 10. On average, the lowest IoU values were obtained for case 1.  
The visualisation of the damage identification results has been performed in such a way that the delamination ground truth is shown in the green colour, the DL model prediction is shown in the red colour, and the intersection of the two has been made by colour mixing, which gives the yellow colour. Therefore, the more yellow pixels there are, the greater the overlap of the delaminations and, in turn, the better the accuracy.  
It should be noted that despite the low IoU values in certain cases, the identification algorithm performed remarkably well, because the delamination was localised accurately for each scenario.  
The delamination identification results in terms of the IoU values are gathered in Table 2, wherein we also find the results from [11], which have been added for comparison.  
Figure 10: Delamination identification results: green—ground truth, red—prediction, yellow—intersection.  
The method presented in [11] is completely different from the one presented here, but it relies on the same dataset. However, the frame size was larger, namely \((512\times 512)\) pixels versus \((256\times 256)\) pixels here, thus giving a better resolution of the damage identification.  
Although, according to Table 2, the current results are not as good as compared to our previous paper [11], the advantage of the proposed method is that it can be easily extended to cases in which only a limited number of signals are available in comparison to full wavefield data. This is extremely important for practical applications in structural health monitoring where only the signals at sensor locations are available.  
It should also be stressed that the complexity of the proposed DL model and the dataset size used for training are limited by the memory of a single Nvidia Tesla V100 GPU (32 GB memory), which was available to us. Certainly, the surrogate DL model can be improved by using a larger number of frames in the sequence of ConvLSTM layers. It is expected that the damage identification would improve as well with a more accurate surrogate DL model.