Modeling of Elastic Modulus of Jointed Rock Mass: Gaussian Process Regression Approach
Publication: International Journal of Geomechanics
Volume 14, Issue 3
Abstract
The elastic modulus () of a jointed rock mass is an important parameter for rock mechanics. This study examines the capability of Gaussian process regression (GPR) for determination of the of jointed rock masses. The GPR is a Bayesian nonparametric model. The joint frequency (), joint inclination parameter (), joint roughness parameter (), confining pressure (), and elastic modulus () of intact rock are considered as inputs of the GPR. The output of the GPR is the of jointed rock masses. The developed GPR has been compared with the artificial neural network (ANN) models. Variance of the predicted of jointed rock masses is obtained from the GPR. The results show that the developed GPR is a promising tool for the prediction of the of jointed rock masses.
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Acknowledgments
The authors thank T. G. Sitharam and Vidya Bhushan Maji for providing the data.
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Published In
International Journal of Geomechanics
Volume 14 • Issue 3 • June 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Dec 1, 2012
Accepted: May 17, 2013
Published online: May 20, 2013
Published in print: Jun 1, 2014
Discussion open until: Aug 5, 2014
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