# Modeling Undrained Shear Strength of Sensitive Alluvial Soft Clay Using Machine Learning Approach
## 3 Results and Discussion  
For the examined soft clay soil, descriptive statistics of its geotechnical parameters, as well as the linear correlation chart, are illustrated in Figure 5. The studied clay soils exhibited a wide range of dry density (8.93-12.4 kN/m\({}^{3}\)), initial void ratio (1.149-2.017), specific gravity (2.68-2.74), consistency index (0.06-1.15), and pocket penetration shear (10-80 kPa). Table 1 summarizes the statistical analysis of the dataset. The increments in the sensitive clay initial properties, like dry unit-weight, liquid limit, plasticity index, and consistency index, caused an increase in the values of the undrained shear strength, as illustrated in Figure 5. The undrained shear strength expressed the cohesion shear strength parameter of the normally consolidated clayey soil. The dry density increase led to additional inter-cohesion between clay particles. Consequently, the increment of liquid limit, plasticity, and consistency indexes was an indication of the high values of clay contents in the studied sensitive soil. The higher value of clay contents caused higher values of cohesion (undrained shear strength). The later results were found also by [7]. On the other hand, the water content and the void ratio had a negative influence on the USS of the sensitive soil. This is because the excessive voids and water in the soil logically decreased the shear resistance of the soil. Finally, the specific gravity had approximately no impact on the soil USS values, as depicted in Figure 5.  
Data partitioning using a procedure known as cross-validation (CV) is essentially required for accurate modeling. Using this technique, the training dataset was randomly partitioned into two groups: the training set and the validation set. The training set was used in the learning phase and was typically the largest dataset and was mainly utilized to obtain the parameters of the model under development, while the validation set was basically used to tune the model hyperparameters. CV assisted data partitioning into a set of folds (k-folds). This process was run either during training or when estimating the average test error across all folds. Subsequently, this technique guards against overfitting better than others; however, it requires numerous fits, and consequently, it is suitable for small and medium-sized datasets. In this modeling approach, we used a CV partitioning of 5 folds. Sequentially, several ML techniques, including linear regression, regression trees, Gaussian process regression, the ensemble of trees, and support vector regression, were trained and validated to attain their optimum hyperparameters. For the SVR, the most important hyperparameters were the box constraint, denoted by the parameter \(C\), which was a positive numeric variable that determined the penalty imposed on samples that fell beyond the epsilon edges (\(\epsilon\)) and assisted in the prevention of overfitting or satisfying model  
Figure 4: Framework of soft clay ML modeling. regularization. In addition, the selection of the kernel function, as well as its optimized kernel scale, plays an important role in scaling the input features. The model performance is further evaluated using a new dataset, namely the testing set. This set is used to assess the model's performance and guarantees that it can generalize effectively to new and unknown data points. The evaluation metrics for RMSE and \(R^{2}\) for both the training and testing sets are listed in Table 2. Among the adopted algorithms, the fine Gaussian SVR gave the best-fit results. The RMSE of the training and testing sets of the optimized model were 2.57 and 1.65 kPa, respectively. The model hyperparameters, including box constraint, \(\varepsilon\), kernel function, and kernel scale of the optimized model based on SVR, are outlined in Table 3. The attained values of the determination coefficient, \(R^{2}\)-Score, were 0.90 and 0.96, respectively.