## 4 Discussion  
The results show that the proposed method regarding the horizontal distribution of specific A-scan features in one B-scan is suitable to classify moisture damages in unknown floor constructions. In a data set of 504 B-scans covering 252 different experimental setups, 84.3% to 88.3% of the scenario's dry, damaged insulation, and damaged screed were recognized correctly by the trained classifiers. A closer investigation of the produced false-negatives often revealed the measurement of undamaged areas which underlines the method's sensitivity and suggest even higher accuracies. In particular, the combination of amplitude and frequency features covering all relevant reflections in the GPR signal contributed to the successful results. Therefore, this study generally proposes an enhanced use of multivariate data analysis when performing moisture measurements with GPR.  
The presented method also worked well as a supporting procedure for the neutron probe. In particular, moisture inside the insulation layer was mostly undetected by the sole use of the radiation measurement, whereas GPR achieved a satisfying sensitivity.  
Since the data set only contained laboratory measurements under controlled conditions, the method still needs to be validated in practical on-site investigations of real damage cases. Here, unknown parameters like an unstable layer thickness or obstructive  
Figure 15: Scatter plots showing the feature combination of \(F_{B}\) & \(F_{C}\) (**left**), \(F_{B}\) & \(F_{D}\) (**middle**) and \(F_{D}\) & \(F_{E}\) (**right**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Origin in A-Scan** & **Score** \\ \hline \(F_{A}\) & \(A_{DW}\) & 0.61 \\ \(F_{B}\) & \(A_{RW2}\) & 1.0 \\ \(F_{C}\) & \(f_{RW2}\) & 0.95 \\ \(F_{D}\) & \(A_{RW1}/A_{DW}\) & 0.48 \\ \(F_{E}\) & \(f_{RW1}/f_{DW}\) & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Achieved scores of the applied B-scan features.  
floor heating pipes could lead to misinterpretations which might produce an increased number of false-positive classifications. Upcoming works by the authors will address these questions. If satisfying accuracies can be achieved, the method will be capable of significantly reducing the need for destructive drilling cores to classify underlying damage scenarios, and therefore cut the costs of renovations.  
Further, potential optimizations could be investigated regarding the classifiers' configurations, since only default parameters have been used so far. In addition, the use of deep learning (ANN) to automatically extract novel, relevant features out of radargrams (b-scans as input parameter) can be examined with the obtained dataset.  
Conceptualization, T.K., C.S., T.R. and S.K.; methodology, T.K., C.S., T.R. and S.K.; software, T.K.; validation, T.K.; formal analysis, T.K.; investigation, T.K.; resources, C.S., T.R. and S.K.; data curation, T.K.; writing--original draft preparation, T.K.; writing--review and editing, C.S. and S.K.; visualization, T.K.; supervision, C.S. and S.K.; project administration, C.S.; funding acquisition, C.S. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank [PERSON] and [PERSON] for their helpful comments on the text. For their support in producing the screed samples, gratitude is owed to [PERSON] and [PERSON].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ANN Artificial neural network  
CA Anhydrite  
CE Civil engineering  
CT Cement  
DAQ Data acquisition  
CPR Ground penetrating radar  
DW Direct Wave  
EM Electromagnetic  
EP Expanded polysterene  
GW Glass wool  
MLR Multinomial logistic regression  
NMR Nuclear magnetic resonance  
PS Perlites  
R Receiver  
RF Random forest  
RW Reflection Wave  
STFT Short-time Fourier transform  
SVM Support vector machine  
Transmitter  
XP Extruded polysterene