## 3 Materials and Methods
For the evaluation of the goodness of fit of the models, the standard error of \(y\), estimated based on \(x\)\(\left(S_{e}\right)\), the standard deviation \(\left(S_{y}\right)\), and the adjusted coefficient of determination were calculated for all regression models. The adjusted \(R_{*}^{2}\) is a modification of \(R^{2}\) considering the number of observations and the number of explanatory variables. The formulae are as follows:  
\[S_{e}=\sqrt{\frac{\Sigma(y-\hat{y})^{2}}{n-k-1}} \tag{11}\]  
\[S_{y}=\sqrt{\frac{\Sigma(y-\overline{y})^{2}}{n-1}} \tag{12}\]  
\[R_{*}^{2}=1.0-\left(\frac{S_{e}}{S_{y}}\right)^{2} \tag{13}\]  
where \(y\) is the measured value, \(\hat{y}\) is the predicted value, \(\overline{y}\) is the average of the measured values, \(n\) is the sample size, and \(k\) is the number of independent variables. A lower \(S_{e}/S_{y}\) value and a higher \(R_{*}^{2}\) value indicate better agreement between the predicted and measured data. Based on previous research [38; 39; 40], an excellent model fit is achieved if the value of \(S_{e}/S_{y}\) is below 0.35 and \(R_{*}^{2}\) is above 0.9, as shown in Table 1.  
Figure 2: Measuring the relative permittivity of gyratory specimens in the laboratory using a PaveScan device. After conducting the laboratory tests, the relative permittivity of the built-in asphalt layers was measured in situ using a PaveScan non-destructive device, which was developed for asphalt density evaluation (Figure 3). The device has three 2 GHz frequency air-coupled sensors at an arbitrary distance from each other, albeit with a maximum distance of 1.1 m, in order to properly cover the road surface. The sensors were fixed at a 30 cm height above the surface. The sensors automatically calculate the relative permittivity of a given point on the asphalt surface based on the Surface Reflection (SR) method. The device performs 40 scans per metre; therefore, it is possible to perform a high-resolution determination of the relative permittivity. The measurement process starts with air calibration (Figure 3, left) to eliminate background noises, and metal plate calibration (Figure 3, right) to detect the amplitudes of electromagnetic waves reflected from the asphalt surface and from the metal plate. Three measurements were performed on the entire experimental road section using the PaveScan device. The first (#1) measurement was performed after constructing the AC 22 asphalt base course, the second (#2) measurement was performed after constructing the AC 22 asphalt binder course, and finally, the third (#3) measurement was performed after constructing the AC 11 asphalt wearing course. By performing the measurements on every layer, information was acquired regarding the relative permittivity of all asphalt layers. To cover the entire carriageway, two measurements were performed on each lane, one at the lane edge (using the outside sensor near the shoulder) and another at the middle of the lane (using the outside sensor near the road axis). To acquire more detailed data, these measurements were complemented with a measurement along the road axis (Figure 4). For the validation of the GPR measurement results, drilled core samples were obtained and analysed. After constructing the asphalt AC 22 base course and the asphalt AC 22 binder course, 3-3 core samples were extracted on the right and left lanes at about -70 m apart (Figure 5). \begin{table}
\begin{tabular}{c c c} \hline
**Criteria** & \(\mathbf{R_{\star}^{2}}\) & \(\mathbf{S_{e}/S_{y}}\) \\ \hline Excellent & \(\geq\)0.90 & \(\leq\)0.35 \\ Good & 0.70–0.89 & 0.36–0.55 \\ Fair & 0.40–0.69 & 0.56–0.75 \\ Poor & 0.20–0.39 & 0.76–0.89 \\ Very Poor & \(\leq\)0.19 & \(\geq\)0.90 \\ \hline \end{tabular}
\end{table} 
Table 1: Criteria for goodness of fit. Figure 3: PaveScan device during air calibration (**left**) and metal plate calibration (**right**). However, in the case of the asphalt AC 11 wearing course, only one in-site drilling was performed. The bulk density of the core samples was determined using the AASHTO T 275-A method.