Journal
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
Volume 58, Issue -, Pages 61-72Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijrmms.2012.10.002
Keywords
Multiple regression; Artificial neural network; Multi layer perceptron; Radial basis function; Rock properties; Equivalent sound level; Rock drilling
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This study aims to predict rock properties using soft computing techniques such as multiple regression, artificial neural network (MLP and RBF) models, taking drill bit speed, penetration rate, drill bit diameter and equivalent sound level produced during drilling as the input parameters. A database of 448 cases were tested for determination of uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (rho), P-wave velocity (V-p), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) and the prediction capabilities of the models were then analyzed. Results from the analysis demonstrate that neural network approach is efficient when compared to statistical analysis in predicting rock properties from the sound level produced during drilling. (C) 2012 Elsevier Ltd. All rights reserved.
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