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Frontiers of Structural and Civil Engineering

Front Struc Civil Eng    2013, Vol. 7 Issue (2) : 133-136
Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach
Pijush Samui(), Jagan J
Centre for Disaster Mitigation and Management, VIT University, Vellore-632014, India
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This article examines the capability of Gaussian process regression (GPR) for prediction of effective stress parameter (χ) of unsaturated soil. GPR method proceeds by parameterising a covariance function, and then infers the parameters given the data set. Input variables of GPR are net confining pressure (σ3), saturated volumetric water content (θs), residual water content (θr), bubbling pressure (hb), suction (s) and fitting parameter (λ). A comparative study has been carried out between the developed GPR and Artificial Neural Network (ANN) models. A sensitivity analysis has been done to determine the effect of each input parameter on χ. The developed GPR gives the variance of predicted χ. The results show that the developed GPR is reliable model for prediction of χ of unsaturated soil.

Keywords unsaturated soil      effective stress parameter      Gaussian process regression (GPR)      artificial neural network (ANN)      variance     
Corresponding Authors: Samui Pijush,   
Issue Date: 05 June 2013
 Cite this article:   
Pijush Samui,Jagan J. Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach[J]. Front Struc Civil Eng, 2013, 7(2): 133-136.
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Pijush Samui
Jagan J
Fig.1  Performance of training data set
Fig.2  Performance of testing data set
Fig.3  Comparisons between ANN and GPR
Fig.4  Sensitivity analysis of the input parameters
Fig.5  Variance of training data set
Fig.6  Variance of testing data set
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