Frontiers of Structural and Civil Engineering
Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach
Centre for Disaster Mitigation and Management, VIT University, Vellore-632014, India
This article examines the capability of Gaussian process regression (GPR) for prediction of effective stress parameter ( ) 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 ( ), saturated volumetric water content ( ), residual water content ( ), bubbling pressure ( ), suction ( ) and fitting parameter ( ). 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 . The developed GPR gives the variance of predicted . The results show that the developed GPR is reliable model for prediction of of unsaturated soil.