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Frontiers of Structural and Civil Engineering

Front Struc Civil Eng    2013, Vol. 7 Issue (2) : 133-136     https://doi.org/10.1007/s11709-013-0202-1
RESEARCH ARTICLE |
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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Abstract

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,Email:pijush.phd@gmail.com   
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.
 URL:  
http://journal.hep.com.cn/fsce/EN/10.1007/s11709-013-0202-1
http://journal.hep.com.cn/fsce/EN/Y2013/V7/I2/133
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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
1 Bishop A W. The principle of effective stress. Teknisk Ukeblad , 1959, 106(39): 859–863
2 Khalili N, Khabbaz M H. A unique relationship for the determination of the shear strength of unsaturated soils. Geotechnique , 1998, 48(5): 681-687
doi: 10.1680/geot.1998.48.5.681
3 Russell A R, Khalili N A. Unified bounding surface plasticity for unsaturated soils. International Journal for Numerical and Analytical Methods in Geomechanics , 2006, 30(3): 181–212
doi: 10.1002/nag.475
4 Garven E N, Vanapalli S K. Evaluation of empirical procedures for predicting the shear strength of unsaturated soils. In: Proceedings of the Unsaturated Soils . ASCE, Sharma and Singhal, 2006, 2570–81
5 Fazeli A, Habibagahi G. Ghahramani A V. Shear strength characteristics of Shiraz unsaturated silty clay. Iranian Journal of Science Technology , 2010, 33(B4): 327–341
6 Ajdari M, Habibagahi G, Ghahramani A. Predicting effective stress parameter of unsaturated soils using neural networks. Computers and Geotechnics , 2012, 40: 89–96
doi: 10.1016/j.compgeo.2011.09.004
7 Park D, Rilett L R. Forecasting freeway link ravel times with a multi-layer feed forward neural network. Computer-Aided Civil and Infrastructure Engineering , 1999, 14: 357–367
8 Kecman V. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. Cambridge, MA: MIT Press, 2001
9 A?man K, Kocijan J. Application of Gaussian processes for black-box modelling of biosystems. ISA Transactions , 2007, 46(4): 443–457
doi: 10.1016/j.isatra.2007.04.001
10 Stegle O, Fallert S V, MacKay D J, Brage S. Gaussian process robust regression for noisy heart rate data. IEEE Transactions on Bio-Medical Engineering , 2008, 55(9): 2143–2151
doi: 10.1109/TBME.2008.923118
11 di Sciascio F, Amicarelli A N. Biomass estimation in batch bio-technological processes by Bayesian Gaussian process regression. Computers & Chemical Engineering , 2008, 32(12): 3264–3273
doi: 10.1016/j.compchemeng.2008.05.015
12 Yuan J, Wang K, Yu T, Fang M. Reliable multi-objective optimization of highspeed WEDM process based on Gaussian process regression. International Journal of Machine Tools & Manufacture , 2008, 48(1): 47–60
doi: 10.1016/j.ijmachtools.2007.07.011
13 Pal M, Deswal S. Modelling pile capacity using Gaussian process regression. Computers and Geotechnics , 2010, 37(7-8): 942–947
doi: 10.1016/j.compgeo.2010.07.012
14 Williams K I, Rasmussen C E. Gaussian Processes for Regression, Cambridge, MA: MIT Press, 1996
15 Liong S Y, Lim W H, Paudyal G N. River stage forecasting in Bangladesh: Neural network approach. Journal of Computing in Civil Engineering , 2000, 14(1): 1–8
doi: 10.1061/(ASCE)0887-3801(2000)14:1(1)
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