Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks  

作　　者：DEHGHAN S SATTARI Gh CHEHREH CHELGANI S ALIABADI M A 

机构地区：[1]Mining Engineering Department, Mahallat Branch, Islamic Azad University [2]Surface Science Western, University of Western Ontario

出　　处：《Mining Science and Technology》2010年第1期41-46,共6页矿业科学技术（英文版）

摘　　要：Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.Uniaxial Compressive Strength （UCS） and modulus of elasticity （E） are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks （ANNs）, were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both methods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear relations obtained coefficients of determination of （R2） of 0.64 and 0.56, respectively. ANNs were used to improve the regression results. The generalized regression and feed forward neural networks with two outputs （UCS and E） improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

关 键 词：uniaxial compressive strength modulus of elasticity artificial neural networks regression TRAVERTINE 

分 类 号：TD313[矿业工程—矿井建设]