Soil-cement column combined with geogrid on top or Geogrid Reinforced Pile Supported (GRPS), is used to construct structures on soft ground. Because of its high tensile capacity, the geogrid is spread on the top of the soil-cement column to form a soft transmission layer, increasing the capacity transferred to the columns, reducing a part of the load transmitted to the soft soil between the columns. The numerical analysis results of the GRPS with a high strength geogrid showed four major factors affecting transmission the efficacy of the column (Ef) and the tensile force of the geogrid including effective vertical load (v’); the ratio of the distance between the columns and the column’s diameter (s/D); the ratio of the elastic modulus of the soil-cement column to the deformation modulus of soil (Ec/Es); the tensile stiffness of the geogrid (J). The efficacy of the column (Ef) increases rapidly with an increase in effective vertical load (v’) from 0.23 to 0.44. In contrast, the transmission efficiency (Ef) decreases from 0.60 to 0.37 when s/D increased. When the ratio Ec/Es > 150 and J > 8000 kN/m, the tensile force of the geogrid tends not to change much.

One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). In addition, two popular ML models namely Reduced Error Pruning Trees (REPTree) and Decision Stump (Dstump) were used for comparison. Results showed that the performance of the novel model ANFIS-DE is the best (R = 0.825, MAE = 0.064 and RMSE = 0.094) in comparison to other models such as REPTree (R = 0.7802, MAE = 0.068 and RMSE = 0.0988) and Dstump (R = 0.7325, MAE = 0.0785 and RMSE = 0.1036). Therefore, the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc, which can be employed in the design and construction of civil engineering structures.