TY - JOUR ID - 144 TI - Prediction of the deformation modulus of rock masses using Artificial Neural Networks and Regression methods JO - Journal of Mining and Environment JA - JME LA - en SN - 2251-8592 AU - Gholamnejad, Javad AU - Bahaaddini, HamidReza AU - Rastegar, Morteza AD - Department of mining and metallurgical engineering AD - M.Sc. student, Department of Mining and Metallurgical engineering, Yazd University, Yazd, Iran Y1 - 2013 PY - 2013 VL - 4 IS - 1 SP - 35 EP - 43 KW - Rock mass modulus KW - neural networks KW - Regression method KW - Discontinuity DO - 10.22044/jme.2013.144 N2 - Static deformation modulus is recognized as one of the most important parameters governing the behavior of rock masses. Predictive models for the mechanical properties of rock masses have been used in rock engineering because direct measurement of the properties is difficult due to time and cost constraints. In this method the deformation modulus is estimated indirectly from classification systems. This paper presents the results of a study into the application of Artificial Neural Networks (ANN) technique and Regression models for estimation of the deformation modulus of rock masses. A database, including 225 actual measured deformation modulus, Uniaxial Compressive Strengths of the rock (UCS), and Rock Mass Rating (RMR) was established. Data collected from different projects. For predicting Em by regression, a nonlinear regression method was chosen. This model showed the coefficient correlation of 0.751 and mean absolute percentage error (MAPE) of 9.911%. Also a three-layer ANN was found to be optimum, with an architecture of two neurons in the input layer, four neurons in the hidden layer and one neuron in the output layer. The correlation coefficient determined for deformation modulus predicted by the ANN was 0.786 and the quantity of MAPE was 6.324%. With respect to the results obtained from two models, the ANN technique was shown to be better than the regression model because of its higher accuracy. UR - https://jme.shahroodut.ac.ir/article_144.html L1 - https://jme.shahroodut.ac.ir/article_144_856cce3b13de100ba5d395fc9824b1c4.pdf ER -