## 3 Dataset
The model's predictive capabilities and the degree of fit of the E\({}_{\text{AC}}\) for a certain range depend on the amount of data in that range. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Input and Output Variables for All Data**} \\ \hline
**Variables** & **Min. ** & **Max. ** & **Mean** & **Median** & **St.Dev** & **Count** \\ \hline \(d_{0}\) (mm) & 164.90 & 1383.80 & 445.25 & 397.25 & 171.95 & 462.00 \\ \(d_{300}\) (mm) & 86.20 & 860.30 & 310.03 & 288.00 & 105.29 & 462.00 \\ \(d_{600}\) (mm) & 75.60 & 370.40 & 189.20 & 184.40 & 49.59 & 462.00 \\ \(d_{900}\) (mm) & 56.40 & 222.10 & 119.48 & 118.95 & 28.68 & 462.00 \\ \(d_{1200}\) (mm) & 41.50 & 148.20 & 83.25 & 82.20 & 19.47 & 462.00 \\ \(d_{1500}\) (mm) & 34.30 & 102.80 & 61.92 & 61.85 & 13.65 & 462.00 \\ \(d_{1800}\) (mm) & 27.10 & 83.20 & 49.39 & 48.85 & 10.59 & 462.00 \\ temperature (\({}^{\circ}\)C) & 10.35 & 40.53 & 23.15 & 28.46 & 10.82 & 462.00 \\ EAC (MPa) & 589.00 & 6963.90 & 3394.70 & 3167.55 & 1672.38 & 462.00 \\ \hline \hline \multicolumn{8}{c}{**Input and Output Variables for Training Set**} \\ \hline
**Variables** & **Min. ** & **Max. ** & **Mean** & **Median** & **St.Dev** & **Count** \\ \hline \(d_{0}\) (mm) & 164.90 & 1383.80 & 445.56 & 397.85 & 171.01 & 438.00 \\ \(d_{300}\) (mm) & 86.20 & 860.30 & 310.15 & 288.60 & 104.64 & 438.00 \\ \(d_{600}\) (mm) & 75.60 & 370.40 & 189.32 &  
The correlation coefficient R between the backcalculated and predicted E\({}_{\text{AC}}\) values is higher than 0.96 for all models. The coefficient of determination R\({}^{2}\) between the backcalculated and predicted E\({}_{\text{AC}}\) values is higher than 0.93 for all models. Furthermore, the ANN model is marked by a RMSE of 0.066 and a MAPE of 9.13%. The SVM model has a RMSE value of 0.059 and MAPE value of 7.64%, and the RBT model has a 0.078 RMSE and MAPE value of 8.84%. These results indicate that the model of the SVM method features better E\({}_{\text{AC}}\) prediction abilities than the other two analyzed methods. However, a rather small difference in the performance results obtained between the ANN, SVM and BRT methods shows that all three methods provide similar E\({}_{\text{AC}}\) prediction capabilities. Similar research was presented by authors [PERSON] [21], [PERSON] [24] and [PERSON] [25]. In their papers, they analyzed the following ML methods: ANN [21, 24, 25], SVM [24, 25] and BRT [25]. Based on their research, the coefficients of determination (R\({}^{2}\)  
Figure 8: Comparison of predicted E\({}_{\text{AC}}\) with backcalculated E\({}_{\text{AC}}\)—Model SVM. Figure 7: Comparison of predicted E\({}_{\text{AC}}\) with backcalculated E\({}_{\text{AC}}\)—Model ANN. Figure 9: Comparison of predicted E\({}_{\text{AC}}\) with backcalculated E\({}_{\text{AC}}\)—Model BRT. shown in Table 6 were obtained. In all three works, the results were analyzed based on R\({}^{2}\) and therefore compared in this analysis. Having presented the data results from the literature, it can be noticed that the best dependence was achieved in the paper [24]; that is, the error was the smallest. The difference between this work and our research is that, in addition to the deflection values, the input data also included the values of the thickness of the pavement structure layers, which contributes to the accuracy of the analysis. In their research, [PERSON] et al. [21] used deflections too, but also the difference between the deflections of the surface geophone and the geophone at a certain depth. They used different combinations of inputs and based on that, the R\({}^{2}\) of 0.9477 was obtained, which is the approximate value obtained in this paper. The analyzed methods presented in this paper were also observed by the author [PERSON] [25]. [PERSON] used deflections for the input parameters, and as a result, the obtained determination coefficients were lower than the ones obtained in this research.