A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio

Pijush Samui; Dwarkadas Pralhaddas Kothari

doi:10.4186/ej.2012.16.5.69

A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio

Authors

Pijush Samui VIT University
Dwarkadas Pralhaddas Kothari Raisoni Group of Institutions

DOI:

https://doi.org/10.4186/ej.2012.16.5.69

Keywords:

Maximum shear modulus, minimum damping ratio, multivariate adaptive regression spline, prediction, adaptive neuro-fuzzy inference system, multi-layer perceptron.

Abstract

This study uses multivariate adaptive regression spline (MARS) for determination of maximum shear modulus (G_max) and minimum damping ratio (ξ_min) of synthetic reinforced soil. MARS employs confining pressure (σ, psi), rubber (r, %) and sand (s, %) as input variables. The outputs of the MARS are G_max and ξ_min. The developed MARS gives equations for determination of G_max and ξ_min. The results of MARS have been compared with the adaptive neuro-fuzzy inference system (ANFIS), multi-layer perception (MLP) and multiple regression analysis method (MRM). A sensitivity analysis has been also carried out to determine the effect of each input variable on G_max and ξ_min. This study shows that the developed MARS is a robust model for prediction of G_max and ξ_min.

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Author Biographies

Pijush Samui
Google Scholar

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

Dwarkadas Pralhaddas Kothari
Google Scholar

Raisoni Group of Institutions, Nagpur, India

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Published In

Vol 16 No 5, May 27, 2012

How to Cite

[1]

P. Samui and D. P. Kothari, “A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio”, Eng. J., vol. 16, no. 5, pp. 69-78, May 2012.

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