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Modeling of Elastic Modulus of Jointed Rock Mass: Gaussian Process Regression Approach

Kumar, Manoj / Bhatt, Madhav R. / Samui, Pijush

Abstract

The elastic modulus ( $E_{j}$ ) 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 $E_{j}$ of jointed rock masses. The GPR is a Bayesian nonparametric model. The joint frequency ( $J_{n}$ ), joint inclination parameter ( $n$ ), joint roughness parameter ( $r$ ), confining pressure ( $σ_{3}$ ), and elastic modulus ( $E_{i}$ ) of intact rock are considered as inputs of the GPR. The output of the GPR is the $E_{j}$ of jointed rock masses. The developed GPR has been compared with the artificial neural network (ANN) models. Variance of the predicted $E_{j}$ 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 $E_{j}$ of jointed rock masses.

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Modeling of Elastic Modulus of Jointed Rock Mass: Gaussian Process Regression Approach

Kumar, Manoj / Bhatt, Madhav R. / Samui, Pijush

Abstract

The elastic modulus ( $E_{j}$ ) 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 $E_{j}$ of jointed rock masses. The GPR is a Bayesian nonparametric model. The joint frequency ( $J_{n}$ ), joint inclination parameter ( $n$ ), joint roughness parameter ( $r$ ), confining pressure ( $σ_{3}$ ), and elastic modulus ( $E_{i}$ ) of intact rock are considered as inputs of the GPR. The output of the GPR is the $E_{j}$ of jointed rock masses. The developed GPR has been compared with the artificial neural network (ANN) models. Variance of the predicted $E_{j}$ 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 $E_{j}$ of jointed rock masses.

Modeling of Elastic Modulus of Jointed Rock Mass: Gaussian Process Regression Approach

Kumar, Manoj / Bhatt, Madhav R. / Samui, Pijush

Abstract

The elastic modulus ( $E_{j}$ ) 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 $E_{j}$ of jointed rock masses. The GPR is a Bayesian nonparametric model. The joint frequency ( $J_{n}$ ), joint inclination parameter ( $n$ ), joint roughness parameter ( $r$ ), confining pressure ( $σ_{3}$ ), and elastic modulus ( $E_{i}$ ) of intact rock are considered as inputs of the GPR. The output of the GPR is the $E_{j}$ of jointed rock masses. The developed GPR has been compared with the artificial neural network (ANN) models. Variance of the predicted $E_{j}$ 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 $E_{j}$ of jointed rock masses.

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Title:

Modeling of Elastic Modulus of Jointed Rock Mass: Gaussian Process Regression Approach

Contributors:

Kumar, Manoj (author) / Bhatt, Madhav R. (author) / Samui, Pijush (author)

Published in:

International Journal of Geomechanics ; 14

Publication date:

2013-05-20

ISSN:

1532-3641 , 1943-5622

DOI:

https://doi.org/10.1061/(ASCE)GM.1943-5622.0000318

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

Unknown

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