## 3 Results
### Damage Scenario Classification  
Table 2 shows the achieved mean accuracies with the standard deviation of all trained classifiers mentioned in Section 2.6. By using the features presented in Section 2.5, all algorithms were capable of correctly recognizing 84.3% to 88.3% of the considered damage scenarios, without further knowledge about the underlying material or layer thickness. With regard to the broad variations considered in this data set, these accuracies are quite satisfying. To provide a better understanding of the presented results, Figure 12 shows confusion matrices containing each used classifier for the individual layer thicknesses of insulation and scred. For a perfect classification with 100% accuracy, all confusion matrix cells (entries) except the main diagonal would be zero, which means that every scenario would have been classified correctly. Knowing that, the highest deviation of that perfect case gets immediately visible in Figure 12, which lies in the mid column of the top left matrix. It shows that more than half of the measured scenarios with a damaged insulation of 2 cm thickness were classified as dry. This can be explained by the low amount of water (around 0.5 L), that was necessary to cause relative humidities above 80%. Especially for GW and PS, the inserted water was absorbed by the outer edges and did not penetrate into measured areas. As a reference, Figure 13 again shows the flipped GW plate after the measurement with no signs of water ingress on the bottom edge (insulation joint between quadrant IV and I). Due to the significant number of unaffected B-scans, the classification results in Table 2 also show the accuracies for the excluded 2 cm insulation. All classifiers achieved a higher score and comparable standard deviations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Accuracy * (\%)**} \\  & **Mean** & **Std** & **Mean** & **Std** \\ \hline MLR & 86.4 & 3.0 & 89.7 & 3.1 \\ RF & 88.3 & 3.7 & 92.2 & 2.6 \\ SVM & 84.3 & 3.3 & 86.6 & 3.9 \\ ANN & 88.2 & 3.6 & 93.5 & 2.5 \\ \hline \hline \end{tabular}  
* Cases with insulation depth of \(D=2\) cm excluded.  
\end{table} 
Table 2: Statistical comparison (\(k=20\)-fold cross-validation) of the achieved accuracies for all trained classifiers. Figure 11: Water ingress (dark dyeing) in the 6 cm GW insulation. The pictures show the respective bottom of each used insulation plate (40 cm \(\times\) 40 cm \(\times\) 2 cm) in quadrant IV. Figure 12: Combined confusion matrices for the individual insulation (green) and screed (gray) thicknesses considered in the experiment. The classifier’s accuracies within one cell are presented in the same order as in Table 2. Rows and columns include the actual and the predicted (\({}^{\star}\)) scenario, respectively. The blue confusion matrix summarizes the overall accuracies for each scenario. Additionally, for insulations of 6 cm thickness which only included GW and PS, the respective confusion matrix contains 8.3% to 37.5% false-negatives for damaged insulations. Since the GW of 6 cm already showed a measurable influence in Figures 10 and 11, the wrongly classified scenarios are located in the PS data set. In fact, survey line 1 for PS of 6 cm thickness presents a smooth reflection pattern, which is exemplarily shown in Figure 14b). Unfortunately, the structure of PS did not allow referencing pictures like for GW; however, the similarity between dry and damaged insulation suggests that no water penetrated in the measured area. In general, most of the wrong classifications are false-negatives, which are represented by entries left of the main diagonals. Besides the mentioned reasons for damaged insulations, the damaged screed scenario also shows around 5% to 20% of measurements that were classified as dry in nearly every confusion matrix. With regard to the mostly homogeneous reflection patterns shown in Figures 10 and 14, these results are rather satisfying.