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Multivariate Adaptive Regression Spline (Mars) for Prediction of Elastic Modulus of Jointed Rock Mass

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Abstract

This article presents multivariate adaptive regression spline (MARS) for determination of elastic modulus (Ej) of jointed rock mass. MARS is a technique to estimate general functions of high-dimensional arguments given sparse data. It is a nonlinear and non-parametric regression methodology. The input variables of model are joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (σ3) and elastic modulus (Ei) of intact rock. The developed MARS gives an equation for determination of Ej of jointed rock mass. The results from the developed MARS model have been compared with those of artificial neural networks (ANNs) using average absolute error. The developed MARS gives a robust model for determination of Ej of jointed rock mass.

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Acknowledgments

Author thanks T. G. Sitharam and Vidya Bhushan Maji, for providing the data.

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  1. Centre for Disaster Mitigation and Management, VIT University, Vellore, 632014, India

    Pijush Samui

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  1. Pijush Samui
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Correspondence to Pijush Samui.

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Samui, P. Multivariate Adaptive Regression Spline (Mars) for Prediction of Elastic Modulus of Jointed Rock Mass. Geotech Geol Eng 31, 249–253 (2013). https://doi.org/10.1007/s10706-012-9584-4

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  • DOI: https://doi.org/10.1007/s10706-012-9584-4

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