Faculty Publications
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Publications by NITK Faculty
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Item Prediction of penetration rate and sound level produced during percussive drilling using regression and artificial neural network(2012) Kivade, S.B.; Murthy, C.S.N.; Vardhan, H.The main objective of this investigation is to develop a general prediction model and to study the effect of predictor variables such as uniaxial compressive strength, air pressure and thrust on penetration rate and sound level produced during percussive drilling of rocks. The experiment was carried out using three levels Box-Behnken design with full replication in 15 trials. Modeling was done using artificial neural network (ANN) and multipleregression analysis (MRA). These techniques can be utilized for the prediction of process parameters. Comparison of artificial neural network and multiple linear regression models was made and found that error rate was smaller in ANN than that predicted by MRA in terms of sound level and penetration rate. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.Item Influence of index angle on specific energy in rock indentation test(CAFET INNOVA Technical Society 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2015) Kalyan, B.; Murthy, C.S.N.; Choudhary, R.P.In the present work, static indentation tests were carried out in six types of rocks namely pink marble, limestone, basalt, steel gray granite, moon white granite and black galaxy granite using commercial drill bits of 35mm, 38mm, 45mm, 48mm diameters, aimed to know the influence of index (rotation)angle on specific energy. From the experimental data, Force-Penetration (F-P) curves were plotted and Specific energy (energy necessary to excavate a unit volume of rock) values were calculated from F-P curves for each bit rock combinations. The specific energies for the rocks (pink marble, limestone) at 30° index angle were found to be much less than the specific energies at other index angles. Similarly the specific energies for the rocks (basalt, steel gray granite, moon white granite and black galaxy granite) at 20° index angle were found to be much less than the specific energies at other index angles. © 2015 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Estimating rock properties using sound signal dominant frequencies during diamond core drilling operations(Chinese Academy of Sciences rockgeotech@whrsm.ac.cn, 2019) Vijaya Kumar, C.V.; Vardhan, H.; Murthy, C.S.N.; Karmakar, N.C.In many engineering applications such as mining, geotechnical and petroleum industries, drilling operation is widely used. The drilling operation produces sound by-product, which could be helpful for preliminary estimation of the rock properties. Nevertheless, determination of rock properties is very difficult by the conventional methods in terms of high accuracy, and thus it is expensive and time-consuming. In this context, a new technique was developed based on the estimation of rock properties using dominant frequencies from sound pressure level generated during diamond core drilling operations. First, sound pressure level was recorded and sound signals of these sound frequencies were analyzed using fast Fourier transform (FFT). Rock drilling experiments were performed on five different types of rock samples using computer numerical control (CNC) drilling machine BMV 45 T20. Using simple linear regression analysis, mathematical equations were developed for various rock properties, i.e. uniaxial compressive strength, Brazilian tensile strength, density, and dominant frequencies of sound pressure level. The developed models can be utilized at early stage of design to predict rock properties. © 2019 Institute of Rock and Soil Mechanics, Chinese Academy of SciencesItem Artificial neural network for prediction of rock properties using acoustic frequencies recorded during rock drilling operations(Springer Science and Business Media Deutschland GmbH, 2022) Vijaya Kumar, C.V.; Vardhan, H.; Murthy, C.S.N.Determining properties of rocks in rock mechanics/engineering applications such as underground tunnelling, slope stability, foundations, dam design and rock blasting is often difficult due to the requirements of high quality of core rock samples and accurate test apparatus. Prediction of the geomechanical properties of rock material has been an area of interest for rock mechanics for several years now. Nowadays, soft computing methods are used as a powerful tool to estimate the rock properties, cost and duration of the project. This has led to a lack of necessity to develop a model to predict rock properties in the field of rock mechanics. ANN (artificial neural network) models were developed to predict geomechanical properties of the sedimentary rock types using dominant frequencies of an acoustic signal during rock drilling operations. A set of experimental drilling operations test conditions around 875 were used as input parameters including drill bit spindle speeds (rpm), drill bit penetration rates (mm/min), drill bit diameters (mm) and dominant frequencies of the acoustic signal (Hz). The response (output) was uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), density (ρ) and abrasivity (%). The developed models were checked using various performance indices. The results from the analysis show that the suggested ANN model approach is efficient, fits the data and accurately reflects the experimental results. The ANN models predicted physico-mechanical rock properties through the dominant frequency of acoustic signals during rock drilling operations. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.Item Thermal Conductivity Assessment in Limestone Rocks: Unveiling Patterns through P-Wave Velocity, Uniaxial Compressive Strength and Mineral Composition(World Researchers Associations, 2025) Dileep, G.; Kumar, T.A.; Murthy, C.S.N.; Labani, R.; Kumar, P.S.Rock thermal conductivity is a critical property in the building and construction industry, playing a key role in optimizing energy efficiency. It guides material selection for insulation and ensures effective resistance to heat transfer within structures. This study introduces an alternative approach for estimating the thermal conductivity of rocks using an indirect method. The proposed approach leverages P-wave velocity, uniaxial compressive strength and mineral composition as predictive parameters. This study examines the relationship between thermal conductivity and key rock properties, including P-wave velocity, uniaxial compressive strength and quartz content. A significant positive correlation was identified, highlighting the potential of these parameters as reliable predictors for estimating the thermal conductivity of rocks. © 2025, World Researchers Associations. All rights reserved.