## 3 Results
### Bearing Capacity Analysis Results  
Multiple linear regression analysis was performed to develop a prediction model for bearing capacity based on soil parameters. The results including model summary, ANOVA table, and regression coefficients are detailed below. The model yielded a value of 0.460, indicating that about 46% of the variance in bearing capacity was explained by the predictors. However, the adjusted value was negative (\(-0.979\)), reflecting overfitting or inclusion of non-contributing variables (Table 4). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Model Summary b**} \\ \hline \multirow{3}{*}{**Model**} & \multirow{3}{*}{**R**} & \multirow{3}{*}{**R**} & \multirow{3}{*}{**Adjusted**} & \multirow{3}{*}{**Std. Error of**} & \multicolumn{6}{c}{**Change Statistics**} \\ \cline{4-10}  & & & & & & **R Square** & & **F** & **R Square** & **F** & **Adjusted** & **df1** & **df2** & **Sign. F** \\ \cline{1-1} \cline{3-10}  & & & & & & **Change** & & **Change** & & **Change** & & **Change** \\ \hline \(1\) & \(0.678\) a & \(0.460\) & \(-0.979\) & \(1.29345\) & \(0.460\) & \(0.320\) & \(8\) & \(3\) & \(0.912\) \\ \hline \hline \end{tabular}  
* \({}^{\text{a}}\): Predictors: (Constant), cohesion, sieve no. 200, water content, excavation level, sieve no. 10, unit volume weight, groundwater level, internal friction angle; b: Dependent Variable: Bearing Capacity.  
\end{table} 
Table 4: Multiple linear regression analysis results for Bearing Capacity. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Bearing** \\ **Capacity** \\ **(MPa)** \\ \end{tabular}  & \begin{tabular}{c} **Excvation** \\ **Level (m)** \\ \end{tabular}  & \begin{tabular}{c} **Ground** \\ **Water** \\ **Level (m)** \\ \end{tabular}  & \begin{tabular}{c} **Unit** \\ **Volume** \\ **Weight** \\ **(g/cm\({}^{3}\))** \\ \end{tabular}  & \begin{tabular}{c} **Water** \\ **Weight** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **Sieve** \\ **Content** \\ **(2 mm)** \\ \end{tabular}  & \begin{tabular}{c} **Sieve** \\ **(0.074)** \\ **mm)** \\ \end{tabular}  & \begin{tabular}{c} **Sieve** \\ **Friction** \\ **Angle** \\ \end{tabular}  & \ \begin{tabular}{c} **Steve No.** \\ **Cohesion** \\ **Coefficient** \\ \end{tabular}  \\ \hline \multirow{3}{*}{ \begin{tabular}{c} Bearing \\ Capacity (MPa) \\ \end{tabular}  } & Correlation & \(1\) & \(0.033\) & \(-0.146\) & \(-0.078\) & \(0.272\) & \(0.132\) & \(0.356\) & \(-0.039\) & \(0.163\) \\ \cline{2-11}  & Sig. & \(0.871\) & \(0.468\) & \(0.699\  
The ANOVA analysis reveals a non-significant model 1 (Table 5), indicating that the predictors together do not significantly explain the variability in carrying capacity. This result suggests that the selected predictors may not be robust enough to predict carrying capacity within the current dataset. The regression coefficients show the relative contributions of individual parameters to the model. Key observations include sieve No. 200 (\(\beta\) = 0.937) showing the highest positive standard beta coefficient, implying a potentially strong relationship with bearing capacity, but which was not statistically significant. The angle of internal friction (\(\beta\) = \(-\)3.138) exhibited a negative relationship, which may suggest a complex interaction with other variables affecting soil strength. Cohesion (\(\beta\) = 2.160) showed a positive but insignificant contribution to bearing capacity. These results suggest that further refinement of the regression model is needed, perhaps through feature selection or transformation, to improve the predictive power (Table 6). Mathematical prediction models (linear, quadratic, and third order) were applied to evaluate the bearing capacity estimation more comprehensively. Figures 4-6 show the actual and estimated bearing capacity values with their respective values. The linear model showed limited accuracy with a value indicating a weak correlation between the estimated and actual bearing capacities.