A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio
Keywords:Maximum shear modulus, minimum damping ratio, multivariate adaptive regression spline, prediction, adaptive neuro-fuzzy inference system, multi-layer perceptron.
This study uses multivariate adaptive regression spline (MARS) for determination of maximum shear modulus (Gmax) and minimum damping ratio (ξmin) of synthetic reinforced soil. MARS employs confining pressure (σ, psi), rubber (r, %) and sand (s, %) as input variables. The outputs of the MARS are Gmax and ξmin. The developed MARS gives equations for determination of Gmax and ξmin. 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 Gmax and ξmin. This study shows that the developed MARS is a robust model for prediction of Gmax and ξmin.
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