Intelligent Prediction Models For UCS Of Cement/Lime Stabilized QLD Soil

Abstract

The study aims to develop proposed predictive formulas for determining the unconfined compression strength (UCS) of cement/lime stabilized Queensland soil based on Multi-Gene Genetic Programming (MGGP) and Artificial Neural Network (ANN). The models evaluate the effect of three independent variables, including the binder type (cement and lime), the binder content, and the curing time, on the UCS of the stabilized soil. The results show that the selected optimal MGGP and ANN models can predict the target values with high correlation coefficients (R-value approximately of 0.992 and 0.998, respectively), and low errors (e.g., RMSE and MAE). The sensitivity analysis of the MGGP and ANN models provide the same results, in which the curing time has the greatest influence on the UCS value, followed by the binder content and binder type. The performances of the MGGP and ANN models are compared based on statistical parameters, several external criteria, and distribution properties. The study finds that both models show their generalization capabilities with robust, powerful, and accurate prediction ability; however, the ANN model slightly outperforms the MGGP model. The proposed predictive equations formulated from the selected optimal MGGP and ANN models could help engineers and consultants to choose the suitable binder and the reasonable amount of binder in the pre-planning and pre-design period.