Soil stabilization is an in situ soil treatment in which soils are mixed with cementitious or other chemical stabilizing agents. Determining the unconfined compressive strength (UCS) of stabilized soil is a principal task in the design and construction of the ground improvement. Hence, this study aims to develop a reliable predictive model for the UCS of clay stabilization with common cementitious binders using the gene-expression programming (GEP) technique. Eleven parameters, including the soil characteristics, the binder types, the binder contents, the mixing method, and the curing period, were considered as the independent variables in the model. The research results show that the selected optimal GEP-based model performs well with an acceptable correlation coefficient (R = 0.951) and low errors (e.g., RMSE and MAE). Besides, parametric analyses indicate that the plastic index, the percentage of clay, and the total water content have a negative effect on the UCS of stabilized soil. In contrast, the percentage of silt and sand, the binder types, the binder contents, and the curing time show a positive effect on the strength of stabilized soil. In addition, the strength of stabilized clay could be significantly enhanced by combining cement with slag, lime, or fly ash with a reasonable ratio, or by reducing the natural water content in the soil. The research findings could help engineers choose suitable binder types and cost-effective methods to optimize the UCS of stabilized clay.

The unconfined compressive strength (UCS) of cement-stabilized soil depends on the particle size distribution, the soil chemistry composition, the cement content, the water content, and the curing time. Determining the UCS of soil-cement material is an important task in the design and construction of infrastructure supported on marginal soils that require ground improvement. In this research, the Artificial Neural Network (ANN) technique was applied to generate the UCS prediction model in relation to the selected parameters. A sensitivity analysis was conducted to examine the range of the contribution of variables. As a result, a statistical analysis shows that the proposed model developed in this study is accurate and reliable with a high correlation coefficient and low root mean squared errors. The UCS prediction model also satisfies well the external criteria; hence it illustrates the great potential of the predictive ability of the ANN technique for geotechnical parameters. The ANN-based model shows that the cement content and the percentage of the soil particle passing sieve 0.5 mm are the most important variables affecting the UCS value. The research results could be used for estimating the UCS of cement-stabilized sandy soil in situ and laboratory conditions.

The study aims to develop a reliable model using gene-expression programming (GEP) technique for estimating the unconfined compressive strength (UCS) of soil stabilization by cement and fly ash. The model considered the effects of several parameters, including the fly ash characteristics such as calcium oxide (CaO) content, CaO/SiO₂ ratio, and loss of ignition. The research results show that the proposed model demonstrates superior performance with a high correlation coefficient (R > 0.955) and low errors. Therefore, the model could be confidently applied in practice for a variety of fly ash qualities. Besides, the parametric study was conducted to examine the effect of fly ash characteristics on the strength of soil stabilization. The study indicates that if the fly ash contains a high amount of calcium oxide, the strength of fly ash stabilized soil is significant. In addition, fly ash could be used in combination with cement to increase the strength of the mixture. A fly ash replacement ratio is suggested from 0.19 to 0.35, corresponding to the total binder used from 10% to 30%. The research findings could help engineers in optimizing the fly ash proportion and estimating the UCS of soil stabilization by cement and fly ash.