Faculty Publications

Now showing 1 - 4 of 4

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.
Evolution of the probability distribution function of shovel–dumper combination in open cast limestone mine using RWB and ANN: a case study
(Springer Science and Business Media Deutschland GmbH, 2019) Kumar, N.S.H.; Choudhary, R.P.; Murthy, C.S.N.
This newsletter affords a new analytic calculation for the shovel–dumper combination in open cast limestone mine evolution of the only and two galaxy probability density function (PDF). To broaden a nonparametric PDF for a combination of shovel and dumper in an open cast limestone mine, the ancient failure statistics which includes time between failure (TBF) of a shovel and dumpers had been accumulated from the mine. Primarily based on the collected TBF, Weibull parameters which include the shape parameter (?), scale parameter (?), and location parameter (?) have been calculated under the K–S test (Kolmogorov–Smirnov test) using Isograph Reliability Workbench (RWB). In addition, the artificial neural network (ANN) version has been developed to predict the PDF for the same shovel–dumper system and compared with the real acquired fee of RWB. It was found that the values of RMSC and R2 had been 5.96e?5 and 0.999 for PDF. The statistical effects showed that the proposed Reliability Isograph Workbench and PDF version correctly predicts PDF for the shovel–dumper system. © 2019, Springer Nature Switzerland AG.
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.
Investigation on Estimation and Prediction of Resistivity of Limestone Rocks based on Physico-Mechanical Properties of Rocks
(World Researchers Associations, 2025) Varalakshmi, P.; Kumar Reddy, S.K.; Murthy, C.S.N.
Prediction of rock resistivity indirectly is of paramount importance in several geophysical and civil engineering applications. Physico-mechanical properties such as p-wave velocity, porosity and dry density tend to have a good correlation with electrical resistivity of rocks. Conventional approaches for measuring resistivity produce results which may consume more time and efforts and are not accessible every location. To overcome this, an Artificial Neural Network (ANN) model was evolved in this study, using Python and TensorFlow. The model was trained using known values to predict electrical resistivity of unknown and similar materials. Actual results of resistivity were compared with resistivity values obtained from ANN model. The obtained values were evaluated for reliability using non-linear regression models. It was observed that predicted resistivity values generated using p-wave velocity were more reliable. Also, validations made based on the ANN model, using mean absolute error (MAE) and average residuals indicate that P-wave velocity is the most reliable predictor, achieving the lowest MAE (4.638) and near-zero residuals (-0.005), while porosity and dry density showed higher errors and weaker correlations. This study revealed that the ANN model developed results in reliable predictions of rock resistivity based on p-wave values. © 2025, World Researchers Associations. All rights reserved.

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