## 6 Results and Discussion
The following are the conclusive outcomes of the validation:  
* High precision: The fact that both the CVr and CVR values were below 5% suggests that our measurement method is highly precise. The small variability indicates that our measurements were consistently close to the mean value for each configuration. * Reliability: The consistency across all defect configurations suggests that our measurement method is robust and reliable. The method was not significantly affected by the specific features of each configuration. * Consistent performance: Regardless of the specific characteristics of the surface defects, our measurements remained stable, repeatable, and reproducible. This implies that our method was capable of producing reliable results under various conditions. * Validation findings: The low variability values are indicative of a well-controlled measurement process. This provides confidence that the method can be effectively used to assess the surface defect volume in concrete samples. Consequently, based on the results gathered in Table 2, a correlation between the experimental and theoretical measurements of the defect volume is evident. From the data in Figure 11a, there is a strong positive linear correlation and high agreement (R\({}^{2}\) = 0.9968) between the experimental measurements of the defect volume by both operators and the theoretical values of the defect volume for each configuration. This is an extremely favorable result that confirms the validity and high accuracy of the experimental measurements with respect to the theoretical values of the defect volume. In addition, in Figure 11b, an extremely strong positive correlation (R\({}^{2}\) = 0.9968) is shown between the experimental index and the theoretical index, suggesting that the experimental SDI accurately reflects variations relative to the theoretical SDI. The experimental SDI is a robust indicator for evaluating the conformity of experimental measurements with theoretical measurements of defect volume. The advantage of the SDI is that it quantifies the defect volume by considering both the spatial extent (surface area) and the depth of defects. This provides a more comprehensive assessment of surface quality, accounting for defects that may not be immediately visible at the surface but can still impact long-term performance. For example, honeycombs, deep cracks, and bugholes in concrete may indicate durability problems that may not necessarily be detected through surface assessment alone. The quantification of the defect volume often has a stronger correlation with material durability than the quantification of the defect surface area. Defects that penetrate deep into a material typically have a more significant impact on durability. Although Configuration C1 had a larger total defect area than that of Configuration C4 (estimated at 32.71 cm\({}^{2}\) and 30.77 cm\({}^{2}\), respectively), the SDI was lower for Configuration C1, with a value equal to 42.4 (42.4% of the volume occupied by defects) compared to the volume of defects occupied by Configuration C4, which was estimated at 46.92%. This was due to the greater depth of defects in C4, which had a more significant impact on the surface quality. In other words, even though C1 had a greater defect surface area, the defects were less deep, resulting in a lower SDI. This comparison highlights that the SDI takes into account the depth of defects, in addition to the surface area, when assessing surface quality. In this case, it demonstrates that the depth of defects can be a determining factor in the quality of a surface. That is, the quality of the sample surfaces is closely related to the defect volume. Actually, Figure 12 demonstrates an excellent correlation between this index and the volume of defects when the surface quality is assessed through the SDI, suggesting that  
Figure 11: Correlation between the experimental and theoretical values: (a) defect volume and (b) SDI.