## 3 Results and Discussion  
### Effect of Initial Dry Unit Weight  
The study investigated the effect of initial dry unit weight on the elasticity modulus (\(E_{t}\)) and unconfined compressive strength (\(q_{u}\)) of sand-clay mixtures with varying percentages of added sand. The initial saturation level was set at 75% for all samples, while the densities were varied for each sample. Figure 12 shows the change in \(q_{u}\) and \(E_{t}\) with the initial dry unit weight for each percentage of sand. The results showed an increase in both \(q_{u}\) and \(E_{t}\) with an increase in the dry unit weight for each sand-added percentage. On the other hand, both \(q_{u}\) and \(E_{t}\) decreased with an increase in the percentage of sand for all added sand percentages. The reason for this trend is that as the percentage of sand increases for the same dry density, the size of the voids in the soil increases, while the amount of clay minerals that have high water affinity decreases, leading to a reduction in suction forces within the pores of the soil. \begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Hyperparameter** & **Value** \\ \hline \multirow{3}{*}{DTR} & max\_depth & 10 \\  & min\_samples\_leaf & 1 \\  & min\_samples\_split & 1 \\ \hline \multirow{3}{*}{RFR} & max\_depth & 12 \\  & min\_samples\_leaf & 1 \\  & min\_samples\_split & 1 \\  & n\_estimators & 70 \\ \hline \multirow{3}{*}{GBR} & subsample & 0.8 \\  & n\_estimators & 100 \\  & min\_samples\_split & 12 \\  & min\_samples\_leaf & 2 \\  & max\_depth & 3 \\  & learning\_rate & 0.6 \\ \hline \multirow{3}{*}{XGBoost} & subsample\_bytes & 0.6 \\  & learning\_rate & 0.2 \\  & max\_depth & 3 \\  & n\_estimators & 200 \\  & subsample & 0.6 \\ \hline \multirow{3}{*}{SVR} & C & 100,000 \\  & epsilon & 0.1 \\  & kernel & rbf \\ \hline \multirow{3}{*}{ANN} & number of neurons in layer1 & 18 \\  & number of neurons in layer2 & 18 \\  & learning\_rate & 0.07 \\  & batch size & 8 \\  & hidden layers function & ReLU \\  & linkage between the hidden \\  & layer and the ultimate output & ReLU \\  & layer function & ReLU \\ \hline \multirow{3}{*}{ANN-GMDH} & number of neurons in layer1 & 20 \\  & number of neurons in layer2 & 16 \\  & learning\_rate & 0.085 \\ \cline{1-1}  & batch size & 41 \\ \cline{1-1}  & hidden layers function & ReLU \\ \cline{1-1}  & linkage between the hidden \\ \cline{1-1}  & layer and the ultimate output & ReLU \\ \cline{1-1}  & layer function & \\ \hline \end{tabular}
\end{table} 
Table 6: Optimal hyperparameters for models predicting ’\(E_{t}\)’. Figure 12A: This graph is a scatter plot with ascending lines reflecting the trend for a certain percentage of added sand (\(F\)s) inside the soil mixture. The \(x\)-axis shows the dry unit weight (\(\gamma_{d}\) kN/m\({}^{3}\)), which ranges from 13 kN/m\({}^{3}\) to 18% kN/m\({}^{3}\), and the y-axis indicates the unconfined compressive strength (\(q_{u}\)), which ranges from 0 to approximately 1100 kPa. The general trend indicates that as dry unit weight increases, so does unconfined compressive strength, especially after exceeding the maximum dry unit weight of the standard proctor test for each sand content. Figure 12B: This graph, like Figure 12A, is a scatter plot with ascending lines, but it illustrates the change in elasticity modulus (\(E_{t}\)). The y-axis has a significantly broader scale, extending from 0 to 90,000 kPa, representing the higher values typical of elasticity modulus measurements. The lines use the same color and style codes as in Figure 12A and  
demonstrate the similar pattern of increasing elasticity modulus with increasing dry unit weight. The sharp rise in modulus indicates that the soil becomes much stiffer and less deformable as it becomes denser. The trend in Figure 12A shows a clear direct relationship between \(\gamma_{d}\) and \(q_{u}\). As \(\gamma_{d}\) increases, \(q_{u}\) increases across all sand content percentages (_Fs_), indicating that density compromises soil strength.