Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines

Potential of support vector machine regression (SVR) technique for strength estimation of geopolymer stabilized clayey soil has been investigated in the present paper. A comprehensive experimental database of 213 soil samples stabilized with ground granulated blast furnace slag (GGBS) based geopolymer binder were used to develop the SVR model. The database contains 28 day unconfined compressive strength (UCS) results of soil samples generated with different combinations of experimental parameters. A comparative study of different kernel function on SVR model performance is discussed. The study showed that SVR is an effective tool for strength prediction of geopolymer stabilized clayey soil. Subsequently, a parametric study with SVR model was conducted to evaluate the effect of input parameters on UCS. Trends of the result obtained from parametric study were found to be in good agreement with previous research findings. Finally, using the SVR model, an empirical approach for strength prediction of GGBS based geopolymer stabilized clayey soil is proposed for practical application purpose.