Determination of Uniaxial Compressive Strength and Modulus of Elasticity of Travertine using Machine Learning Techniques
Manoj Kumar, Pijush Samui, Ajay Kumar, Naithani · 2013
This article adopts machine learning techniques Relevance Vector Machine (RVM), Gaussian Process Regression (GPR) and Minimax Probability Machine Regression (MPMR)} for determination of Uniaxial Compressive Strength (UCS) and the Modulus of Elasticity (E) of Travertine samples. Point load index (Is(50)), porosity (n), Pwave velocity (Vp), and Schmidt hammer rebound number (Rn) have been taken as inputs of the RVM, GPR and MPMR model. The outputs of RVM, MPMR and GPR are UCS and E. The developed RVM gives equations for prediction UCS and E. The performance of GPR, MPMR and RVM has been compared with the Artificial Neural Network (ANN) models. The simulation results show that the proposed methods give encouraging performance for prediction of UCS and E of Travertine samples.