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

Simple item page
dc.contributor.authorRajesh Kumar, B.
dc.contributor.authorVardhan, H.
dc.contributor.authorGovindaraj, M.
dc.contributor.authorVijay, G.S.
dc.date.accessioned2026-02-05T09:35:05Z
dc.date.issued2013
dc.description.abstractThis 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.
dc.identifier.citationInternational Journal of Rock Mechanics and Mining Sciences, 2013, 58, , pp. 61-72
dc.identifier.issn13651609
dc.identifier.urihttps://doi.org/10.1016/j.ijrmms.2012.10.002
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/26886
dc.publisherElsevier Ltd
dc.subjectBits
dc.subjectDrills
dc.subjectForecasting
dc.subjectRadial basis function networks
dc.subjectRegression analysis
dc.subjectRock drilling
dc.subjectRocks
dc.subjectSeismic waves
dc.subjectSoft computing
dc.subjectTensile strength
dc.subjectWave propagation
dc.subjectBase function
dc.subjectEquivalent sound levels
dc.subjectMLP model
dc.subjectMultilayers perceptrons
dc.subjectMultiple regressions
dc.subjectRadial base function
dc.subjectRadial basis
dc.subjectRock properties
dc.subjectSoftcomputing techniques
dc.subjectSound's levels
dc.subjectCompressive strength
dc.subjectartificial neural network
dc.subjectcompressive strength
dc.subjectdatabase
dc.subjectdrilling
dc.subjectelastic modulus
dc.subjectmultiple regression
dc.subjectnumerical model
dc.subjectP-wave
dc.subjectpenetration
dc.subjectporosity
dc.subjectprediction
dc.subjectseismic velocity
dc.subjecttensile strength
dc.subjectuniaxial strength
dc.titleRegression analysis and ANN models to predict rock properties from sound levels produced during drilling

Files

Collections

Journal Articles