## 4 Discussion  
This study investigated the bearing capacity and settlement behaviour of gravel soils using a combination of laboratory tests, statistical correlation, and predictive modelling. The results provide valuable information about the complex interactions among soil parameters and their effects on geotechnical properties. The bearing capacity analysis highlighted the varying predictive accuracies of different regression models. The linear regression model explained 46% of the variance in bearing capacity (R\({}^{2}\) = 0.46), indicating moderate accuracy. This indicates that although linear relationships can describe the underlying trends, they fail to capture the full complexity of interactions among soil properties. By including nonlinear terms, the quadratic model improved the predictive capacity, explaining 69% of the variance (R\({}^{2}\) = 0.688). The cubic model achieved the highest accuracy, explaining 79% of the variance (R\({}^{2}\) = 0.79), demonstrating its ability to effectively model complex and higher-order interactions.  
These results highlight the importance of nonlinear modelling in geotechnical studies, especially for granular soils such as gravel, which are affected by parameters such as cohesion, internal friction angle, and grain size distribution. Similar trends were observed in previous studies emphasizing the influence of matric suction, overburden tension, and soil expansion on bearing capacity [87,88]. The settlement behaviour of gravel soils exhibited stronger predictive relationships with soil parameters. The linear regression model explained 85% of the variance (R\({}^{2}\) = 0.846), indicating almost very good predictive ability. The quadratic and cubic models further increased the prediction accuracy, each explaining 90.4% of the variance (R\({}^{2}\) = 0.904). These findings indicate that the slump behaviour in gravel soils is less dependent on higher-order interactions than on bearing capacity, probably due to the dominant influence of certain parameters such as excavation depth, unit weight, and grain size distribution (e.g., No. 10 and No. 200 values).  
These findings are consistent with previous studies showing that settlement behaviour in granular soils is more directly affected by fundamental soil properties [82]. The ability of regression models to predict settlement with high accuracy further supports the suitability of empirical approaches for engineering applications. The differences in the accuracy of predictions between bearing capacity and settlement models highlight the different nature of these two parameters. While bearing capacity is affected by a wider range of interactions, settlement behaviour appears to be governed by more direct relationships. These insights highlight the need for specialized approaches when evaluating geotechnical parameters, particularly in the design and analysis of foundations in gravel soils.  
The results also demonstrate that advanced regression models, especially cubic models, can be effectively used in engineering practice to predict soil parameters with high accuracy. This has important implications for the design of foundations, as accurate estimates of bearing capacity and settlement are critical to ensure stability and minimize deformation. Future work can extend this approach to other soil types, such as sandy, clayey, and mixed soils, to develop comprehensive predictive models applicable to a wider range of geotechnical scenarios.