Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
3 results
Search Results
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 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 ANN Models for Prediction of Sound and Penetration Rate in Percussive Drilling(Springer India sanjiv.goswami@springer.co.in, 2015) Kivade, S.B.; Murthy, C.S.N.; Vardhan, H.In the recent years, new techniques such as; Artificial Neural Network (ANN) were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. In this study, ANN models were developed to predict rock properties of sedimentary rock, by using penetration and sound level produced during percussive drilling. The data generated in the laboratory investigation was utilized for the development of ANN models for predicting rock properties like, uniaxial compressive strength, abrasivity, tensile strength, and Schmidt rebound number using air pressure, thrust, bit diameter, penetration rate and sound level. Further, ANN models were also developed for predicting penetration rate and sound level using air pressure, thrust, bit diameter and rock properties as input parameters. The constructed models were checked using various prediction performance indices. ANN models were more acceptable for predicting rock properties. © 2015, The Institution of Engineers (India).