Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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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 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 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).Item Experimentation and statistical prediction of screening performance of coal with different moisture content in the vibrating screen(Routledge, 2022) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.Screening of coal is one of the processes carried out to produce clean coal suitable for the blast furnace. In this work, the screening of moist coal was carried out for different angles of the screen and frequencies. A 2 mm screen perforation was used to separate undersize coal of size +1 mm-2 mm from the +1 mm-3 mm coal samples. For each experimental condition, the screening efficiency was calculated. Maximum screening efficiency of 85.96%, 75.64%, and 63.46% was obtained at 4%, 6%, and 8% moisture content, respectively. As the moisture content of coal increases, the efficiency minimizes due to high screen clogging. After determining the screening efficiency, prediction was carried out using regression modeling. In this work, linear and second-order polynomial regression modeling was utilized to develop a prediction model for the experimental values. From the results, it was clear that the polynomial regression model has high regression coefficient (R2) percentage and low P-value in comparison with the linear regression model. After prediction, validation was carried out on the best fit model. The value of Variance Account For (VAF), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) was in the acceptable range, which shows that the developed model was most effective. © 2020 Taylor & Francis Group, LLC.Item An Artificial Neural Network-Based Approach to Predict Blast-Induced Ground Vibrations in Open Cast Coal Mine— A Case Study(Pleiades Publishing, 2025) Ravikumar, A.; Vardhan, H.; Shankar, M.U.Abstract: This study aims to assess and predict blast-induced ground vibrations of opencast coal mine. The analysis was carried out using two methods i.e. the widely employed empirical vibration predictor known as the USBM (United States Bureau of Mines) equation, and a machine learning model called the artificial neural network (ANN). A dataset including 38 blast vibration recordings was collected and used for the development of an ANN model. Additionally, these datasets were employed to evaluate the site determination constants of the empirical vibration predictor. A total of 27 recordings of blast-induced ground vibrations were gathered from the same opencast coal mine in order to assess the effectiveness of both models. The output (dependent variable) for both models is the peak particle velocity. The effectiveness of the prediction model was evaluated by using commonly used statistical measures, namely the coefficient of determination (). Consequently, the ANN model that was built exhibited more precision in comparison to the existing empirical model. The ANN model exhibited a strong positive relationship between the observed and anticipated peak particle velocity values, as shown by the coefficient of determination (). © Pleiades Publishing, Ltd. 2025.