期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2013
卷号：5
期号：3
出版社：International Center for Scientific Research and Studies
摘要：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), P-wave 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
关键词：Uniaxial Compressive Strength; Modulus of Elasticity; Relevance ;Vector Machine; Gaussian Process Regression; Minimax Probability Machine ;Regression; Artificial Neural Network; Travertine samples
