Regression analysis and ANN models to predict rock properties from sound levels produced during drilling

Kumar, Rajesh B and Vardhan, Harsha and Govindaraj, M and Vijay, G S (2013) Regression analysis and ANN models to predict rock properties from sound levels produced during drilling. International Journal of Rock Mechanics & Mining Sciences, 58. pp. 61-72. ISSN 1365-1609

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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 (r), P-wave velocity (Vp), 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.

Item Type: Article
Additional Information: Copyright © 2013 Elsevier B.V
Uncontrolled Keywords: Multiple regression, Artificial neural network, Multi layer Perceptron, Radial basis function, Rock properties, Equivalent sound level, Rock drilling
Subjects: Engineering > MIT Manipal > Mechanical and Manufacturing
Depositing User: MIT Library
Date Deposited: 20 Mar 2013 11:32
Last Modified: 20 Mar 2013 11:32
URI: http://eprints.manipal.edu/id/eprint/79196

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