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

B. Rajesh Kumar; Harsha Vardhan; M. Govindaraj; G. S. Vijay

doi:10.1016/j.ijrmms.2012.10.002

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

B. Rajesh Kumar, Harsha Vardhan, M. Govindaraj, G. S. Vijay

Department of Mechanical & Industrial Engineering Manipal Institute of Technology, Manipal

Research output: Contribution to journal › Article › peer-review

60 Citations (Scopus)

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 (ρ), 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.

Original language	English
Pages (from-to)	61-72
Number of pages	12
Journal	International Journal of Rock Mechanics and Mining Sciences
Volume	58
DOIs	https://doi.org/10.1016/j.ijrmms.2012.10.002
Publication status	Published - 01-02-2013

All Science Journal Classification (ASJC) codes

Geotechnical Engineering and Engineering Geology

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10.1016/j.ijrmms.2012.10.002

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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 (ρ), 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.",

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AU - Rajesh Kumar, B.

AU - Vardhan, Harsha

AU - Govindaraj, M.

AU - Vijay, G. S.

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

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