## 5 Evaluation and Sensitivity of the Proposed Model  
The proposed GEP model is evaluated statistically in this section to validate its accuracy in predicting the asphalt mixes' RSI value. The statistical measurements R\({}^{2}\), MAE, and RMSE were determined for the training, testing, and all datasets, as shown in Table 6. The low error values and high R\({}^{2}\) values of these datasets and their convergence together indicate the generalization and high level of accuracy of the model in predicting the RSI value. The model also results in an average ratio of \"tested-to-predicted\" RSI of (1.00) and a very low coefficient of variation of (0.841). The model sensitivity in capturing the impact of the input parameters on the predicted RSI values was investigated through a parametric analysis. This type of analysis was found to be a valuable and reliable approach for validating the sensitivity of predictive models [16; 17; 25; 45; 46; 47]. It is based on utilizing the proposed model to predict a dependent variable that corresponds to interchangeable datasets of the input variables. Thus, the GEP model is utilized to predict the RSI values after varying every input parameter in the model while keeping the other three variables constant at their mean values. This approach was repeated in sequence for all parameters, as shown in Figure 8. The obtained trends prove the model's sensitivity and efficiency in predicting the RSI while considering the impact of the input variables, as they confirm the network connection weight results (see Figure 6), where the RSI values increase by increasing the AC values and decreasing the F, Ab, and Va values. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Measures** & **Training Dataset** & **Testing Dataset** & **All Datasets** \\ \hline MAE & 0.537 & 0.508 & 0.530 \\ \hline RMSE & 0.662 & 0.648 & 0.659 \\ R\({}^{2}\) & 0.962 & 0.973 & 0.966 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance measures of the proposed model. Figure 7: GEP expression tree. While the moisture sensitivity of the mixes decreases as the RSI value increases, these plots were elucidated following results from previously published research articles. Figure 8a shows a negative relationship between the mineral filler content and the durability of the asphalt mixes. Previous studies found that incorporating mineral filler in asphalt mixes enhances stability by increasing the stiffness of the asphalt mixes [36; 38]. However, increasing the filler content reduces the asphalt proportion in the same amount of mastic required for coating and lubricating the aggregate particles, resulting in a mixture prone to moisture damage [36]. Figure 8b shows that the RSI values decrease as the moisture-absorption property of the aggregates in the mixtures increases. This can be referred to as the nature of absorptive (porous) aggregates with a high affinity to moisture, facilitating water diffusion into the aggregate-bitumen interface and increasing the severity of moisture damage [27; 30]. It is worth stating that moisture diffusion transforms the moisture-imposed damage from a cohesive to an adhesive mode. The former mode prevails in the dry-conditioned samples where failure is formed within the bulk of asphalt coating aggregates, whereas the latter is dominant in the wet-conditioned samples where the failure is interfacial and mainly weakens the aggregate-bitumen bonds by water intrusion [29; 30]. Figure 8c indicates that the durability of asphalt mixes with high RSI values increases significantly as AC% increases in the mixes. This can be explained by the provided thicker asphalt film that coats the aggregate particles and hence reduces the adverse effect of water on asphalt mixes [31]. Figure 8d shows a concave downward relationship between the RSI values and Va%. This refers to the fact that increasing Va% in asphalt mixes increases their permeability and induces their moisture susceptibility through adhesive failure [1; 2; 7].