## 5 Analysis and Discussions  
In this section, an examination of the acquired data and our attempt at a comparison between the numerical and the experimental results will be presented. The quantitative influence of the presence of the defects caused by the object's ageing process on our capability to detect other sorts of defects will be evaluated. To conduct the analysis, the same region of interest (ROI) that covered the flaw located in the center for each numerical scenario was chosen. When analyzing the experimental results, not only the area that directly covered the defect but also the surrounding area beneath the defect, which was presumed to be defect-free, were considered. In Figure 13, all the chosen regions are presented (red box). Figure 11: The exemplary result of the experiment showing the whole concrete sample with the reinforcement layer. Figure 12: The comparison of the temperature distribution for three time steps (10, 50, and 100 s of heating) shown for the experimental result. In order to assess the acquired data in a quantitative manner, an average value was computed for all the designated regions of interest (Figure 13). Thus, the time-temperature characteristics were determined for the predicted mean temperature values in certain regions. First, the numerical results were compared for samples that did not have any aging defects (referred to as the NDS and CDS samples), samples that had aging defects (referred to as ADS and CASD samples), and the sample that was physically tested in the experiment. In the comparison, both the regions containing the defect and the areas without the defect were taken into account. Figure 14 illustrates all the aforementioned characteristics. Figure 14 presents a comparison of the time-temperature characteristics for the NDS and CDS samples. These characteristics were calculated by computing the average values from the areas marked in Figure 13a,b. The largest disparity between the given features is roughly 1 \({}^{\circ}\)C. The comparison of characteristics between the ADS and CADS samples, conducted by computing the averages for the regions presented in Figure 13c,d, is shown in Figure 14b. Therefore, the existence of defects associated with the ageing of the material has little impact on the temperature distribution in the area of the defect situated at the center of the sample. Figure 14c shows a comparison of analogous characteristics for a real sample that was evaluated via experimentation. In this case, the curves were derived by averaging the data from the specified areas shown in Figure 13e,f. In this instance, the largest disparity between the characteristics is almost 2.3 \({}^{\circ}\)C, which is a noteworthy difference in comparison to the numerical data. It is important to mention that the regions being compared in this context do not completely align with those being studied numerically. Figure 13: The ROI (region of interest) defined for the numerical and experimental results: (**a**) NDS; (**b**) CDS; (**c**) ADS; (**d**) CADS; (**e**) experimental result; (**f**) the region defined for the “no-defect” case for the experimental results. The differences between the results shown in Figure 14a,b are minimal; therefore, in order to quantitatively determine how the presence of aging defects affects the temperature distribution within the region encompassing the centrally located defect, additionally, the difference between the characteristics for the NDS and ADS samples has been plotted (which will show how the presence of aging defects affects the temperature distribution in the central area of the sample when there is no defect there), as it has for the CDS and CADS samples (which will show how the presence of aging defects affects the temperature distribution in the central area of the sample when there is a defect there).