TY - JOUR
T1 - Regression analysis and ANN models to predict rock properties from sound levels produced during drilling
AU - Rajesh Kumar, B.
AU - Vardhan, Harsha
AU - Govindaraj, M.
AU - Vijay, G. S.
PY - 2013/2/1
Y1 - 2013/2/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84869118573&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869118573&partnerID=8YFLogxK
U2 - 10.1016/j.ijrmms.2012.10.002
DO - 10.1016/j.ijrmms.2012.10.002
M3 - Article
AN - SCOPUS:84869118573
SN - 1365-1609
VL - 58
SP - 61
EP - 72
JO - International Journal of Rock Mechanics and Minings Sciences
JF - International Journal of Rock Mechanics and Minings Sciences
ER -