Faculty Publications

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Author: search.filters.author.Govindaraj, M.Subject: search.filters.subject.Compressive strength

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    A new approach for estimation of properties of metamorphic rocks
    (Inderscience Publishers, 2011) Rajesh Kumar, B.R.; Vardhan, H.; Govindaraj, M.
    Rock properties play an important role in the preliminary design of structures. This research focuses on developing empirical models using multiple regression technique for prediction of physical properties of metamorphic rocks. The model considers the following parameters: drill bit diameter, bit speed, penetration rate and equivalent sound level produced during drilling. The F-test was used to check the validity of the developed models. The experimentally measured rock property values and the values calculated from the developed regression model were fairly close which indicates that the developed models could be efficiently used in prediction of intact metamorphic rock properties. Copyright © 2011 Inderscience Enterprises Ltd.
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    Prediction of uniaxial compressive strength, tensile strength and porosity of sedimentary rocks using sound level produced during rotary drilling
    (2011) Rajesh Kumar, B.R.; Vardhan, H.; Govindaraj, M.
    The main purpose of the study is to develop a general prediction model and to investigate the relationships between sound level produced during drilling and physical properties such as uniaxial compressive strength, tensile strength and percentage porosity of sedimentary rocks. The results were evaluated using the multiple regression analysis taking into account the interaction effects of various predictor variables. Predictor variables selected for the multiple regression model are drill bit diameter, drill bit speed, penetration rate and equivalent sound level produced during rotary drilling (Leq). The constructed models were checked using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes. © Springer-Verlag 2011.
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    Sound level produced during rock drilling vis-à-vis rock properties
    (2011) Rajesh Kumar, B.; Vardhan, H.; Govindaraj, M.
    The process of drilling, in general, always produces sound. Though sound is used as a diagnostic tool in mechanical industry, its application in predicting rock property is not much explored. In this study, an attempt has been made to estimate rock properties such as uniaxial compressive strength, Schmidt rebound number and Young's modulus using sound level produced during rotary drilling. For this purpose, a computer numerical controlled vertical milling centre was used for drilling holes with drill bit diameters ranging from 6 to 20. mm with a shank length of 40. mm. Fourteen different rock types were tested. The study was carried out to develop the empirical relations using multiple regression analysis between sound level produced during drilling and rock properties considering the effects of drill bit diameter, drill bit speed and drill bit penetration rate. The F-test was used to check the validity of the developed models. The measured rock property values and the values calculated from the developed regression model are fairly close, indicating that the developed models could be efficiently used with acceptable accuracy in prediction of rock properties. © 2011 Elsevier B.V.
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    Regression analysis and ANN models to predict rock properties from sound levels produced during drilling
    (Elsevier Ltd, 2013) Rajesh Kumar, B.; Vardhan, H.; Govindaraj, M.; Vijay, G.S.
    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 (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. © 2012 Elsevier Ltd.