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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.

neural computing and applications

Effects of binder types and other significant variables on the unconfined compressive strength of chemical-stabilized clayey soil using gene-expression programming