Regression analysis and ANN models to predict rock properties from sound levels produced during drilling
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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
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 (?), P-wave velocity (V<inf>p</inf>), 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. © 2012 Elsevier Ltd.
Description
Keywords
Bits, Drills, Forecasting, Radial basis function networks, Regression analysis, Rock drilling, Rocks, Seismic waves, Soft computing, Tensile strength, Wave propagation, Base function, Equivalent sound levels, MLP model, Multilayers perceptrons, Multiple regressions, Radial base function, Radial basis, Rock properties, Softcomputing techniques, Sound's levels, Compressive strength, artificial neural network, compressive strength, database, drilling, elastic modulus, multiple regression, numerical model, P-wave, penetration, porosity, prediction, seismic velocity, tensile strength, uniaxial strength
Citation
International Journal of Rock Mechanics and Mining Sciences, 2013, 58, , pp. 61-72