Faculty Publications
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Publications by NITK Faculty
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Item 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.Item 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.Item 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.Item A critical review on estimation of rock properties using sound levels produced during rotary drilling(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2012) Masood; Vardhan, H.; Mangalpady, M.; Rajesh Kumar, B.This paper summarizes the critical review on estimation of rock properties using sound levels produced during rotary drilling. In this paper an overall emphasis has been made to summarize the importance of sound level produced during drilling by considering various parameters like drill bit speed, penetration rate, drill bit diameter, type of drill bit and equivalent sound level produced during drilling for the estimation of rock properties. Further an attempt has also made to include the application of ANN modeling and acoustic emission in estimating rock properties. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.Item 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.Item Investigation of Noise Level and Penetration Rate of Pneumatic Drill vis-à-vis Rock Compressive Strength and Abrasivity(Springer India sanjiv.goswami@springer.co.in, 2014) Kivade, S.B.; Murthy, Ch.S.N.; Vardhan, H.In this paper, detailed studies were carried out to determine the influence of rock properties on the sound level produced during pneumatic drilling. Further, investigation was also carried out on the effect of thrust, air pressure and compressive strength on penetration rate and the sound level produced. For this purpose, a fabricated pneumatic drill set up available in the institute was used. Rock properties, like compressive strength and abrasivity, of various samples collected from the field were determined in the laboratory. Drilling experiments were carried out on ten different rock samples for varying thrust and air pressure values and the corresponding A-weighted equivalent continuous sound levels were measured. It was observed that, very low thrust results in low penetration rate. Even very high thrust does not produce high penetration rate at higher operating air pressures. With increase in thrust beyond the optimum level, the penetration rate starts decreasing and causes the drill bit to ‘stall’. Results of the study show that penetration rate and sound level increases with increase in the thrust level. After reaching the maximum, they start decreasing despite the increase of thrust. 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 and abrasivity of sedimentary rocks. The results were evaluated using the multiple regression analysis taking into account the interaction effects of predictor variables. © 2014, The Institution of Engineers (India).Item Quantification of Rock Properties Using Frequency Analysis During Diamond Core Drilling Operations(Springer, 2019) Vijaya Kumar, C.; Vardhan, H.; Murthy, C.S.N.Rock drilling is one of the most essential operations in mining and allied industries. This study focuses on the quantification of physico-mechanical rock properties using dominant frequencies from the sound signal generated through diamond core drilling operations. The rock drilling experiments were performed on five different types of rock samples using a computer numerical control drilling machine. Using simple linear regression analysis, satisfactory mathematical equations were developed between various physico-mechanical rock properties, namely, uniaxial compressive strength, Brazilian tensile strength, density and dominant frequencies of sound level were generated during diamond core drilling operations. The developed models can be utilised for quantification of rock properties with an acceptable degree of accuracy in realistic applications. © 2019, The Institution of Engineers (India).Item Comparison of the prediction performance of separating coal in separation equipment using machine learning based cubic regression modelling and cascade neural network modelling(Taylor and Francis Ltd., 2023) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.The availability of low-grade coal with a high amount of ash has urged the improvisation of separation equipment with minimal or no water utilization. The present work addresses the study on the separation equipment performance with different moisture coal. The experimental results were obtained in terms of separation efficiency. After obtaining the experimental results, the mathematical modeling results were obtained using different techniques. The cubic regression and cascade neural network models were considered to study the mathematical correlation with experimental results. The R-squared value of each mathematical modeling technique was correlated with the model fitting to check the model’s validity. The results clearly showed that the cubic model fitting for the experimental condition had provided an excellent R-squared value varying from 92% to 99%. The cascade model fitting for the experimental condition has provided a higher R-squared value, i.e., more than 99%. Results show that for all experimental conditions, the cascade model fitting of the neural network technique provides the significant mathematical modeling technique suitable for predicting the separation equipment’s performance compared to the cubic model of the regression technique. © 2022 Taylor & Francis Group, LLC.