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

•SVR technique for strength estimation of geopolymer stabilized clayey soil is presented.•Experimental database of 213 soil samples stabilized with slag based geopolymer binder is used.•A comparative study of different kernel function on SVR model performance is discussed.•A parametric study with SV...

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Veröffentlicht in:Construction & building materials 2017-02, Vol.132, p.412-424
Hauptverfasser: Mozumder, Ruhul Amin, Laskar, Aminul Islam, Hussain, Monowar
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Hussain, Monowar
description •SVR technique for strength estimation of geopolymer stabilized clayey soil is presented.•Experimental database of 213 soil samples stabilized with slag based geopolymer binder is used.•A comparative study of different kernel function on SVR model performance is discussed.•A parametric study with SVR model is conducted to evaluate the effect of input parameters on UCS.•An empirical approach for strength prediction of slag based geopolymer stabilized clayey soil is proposed. 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 28day 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.
doi_str_mv 10.1016/j.conbuildmat.2016.12.012
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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 28day 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. 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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 28day 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. 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source Elsevier ScienceDirect Journals Complete
subjects Analysis
Concretes
Geopolymer
Ground-granulated blast furnace slag
Mechanical properties
Portland cement
Soil stabilization
Strength (Materials)
Support vector regression
title Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines
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