[22] documented their findings on the behavior of a GESC-reinforced test embankment on soft soil. As the embankment height rose and the soil consolidated, they observed an increase in the differential settlement between the soil and columns. This was the conclusion they reached after observing the behavior of the test embankment on soft soil enhanced by GESC. The stone columns were typically covered with a 0.3 m or thicker layer of granular sand or gravel to serve as a drainage path and to disperse the loads from the superstructures [23]. [PERSON] et al. [24] proposed a clear and concise theoretical framework for investigating granular mat-topped and granular pile-reinforced soft soil. [PERSON] [25] proposed a mechanical method to estimate the behavior of soft soils stabilized with stone columns and covered with a granular layer. Granular layer reinforcement on the top of stone column-reinforced soil has been found to minimize stress concentration around the top of the columns. Additionally, the granular layer contributes to decreasing the maximum and differential settlement and also improving the load-carrying capacity of the stone column. This allows for enhanced performance on the soft ground. However, because of the complicated geometry and the many uncertainties associated with the geotechnical parameters, it is difficult to determine the bearing capacity of composite foundation systems built with geogrid-reinforced sand beds (GRSB) and with vertically encased stone columns (VESC). Machine learning (ML) approaches have recently attracted much attention for developing paradigms that can precisely predict complex system parameters [12; 26; 27; 28; 29; 30; 31; 32; 33; 34]. [PERSON] and [PERSON] [30] predicted the bearing capacity of stone columns using MATLAB's NeuralNetwork Toolbox. The proposed model showed reliable results, allowing the model to be used as a forecasting tool instead of relying on the results of expensive and time-consuming field or experimental tests. Similarly, [PERSON] and [PERSON] [28] applied the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the bearing capacity of stone columns. The authors developed three models: ANFIS-E, which used experimental results as input; ANFIS-A, which used analytical results as input; and ANFIS-EA, which used both experimental and analytical results as input. Input data were acquired from the existing literature to train the models. For each stone column arrangement and embankment condition, [PERSON] et al. [29] employed Artificial Neural Network systems (ANNs) to predict the settlement under embankment load. Their study utilized soft soil properties together with different geometric factors as input. The ANN model was developed using Ten-Fold Cross-Validation (TFCV) and Non-Cross Validation (NVC). The results showed superior performance of the TFCV model compared with NVC. [PERSON] et al. [31] adopted another ML technique known as Support Vector Machine (SVM) to accurately and effectively predict the bearing capacity behavior of stone columns. In [12], the SVM models were also used to estimate the bearing capacity of geogrid-reinforced stone columns. Three different kernel functions--the polynomial (POLY), radial basis (RBF), and exponential radial basis (ERBF)--served as the foundation for the development and performance evaluation of three different versions of the SVM model. With the optimum generalizability and minimum statistical error of the training and testing data, ERBF-based SVR demonstrated robust performances. Additionally, SVR-ERBF was evaluated against the ANFIS model in predicting the bearing capacity. The authors concluded that the SVR-ERBF model has better performance. In addition, in the same study, the effect of the input parameters on the output predictions from the models was assessed by performing a sensitivity analysis. To forecast the bearing capacity of geogrid-reinforced stone, [PERSON] et al.