Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Vardhan, H.Subject: search.filters.subject.prediction

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Now showing 1 - 7 of 7
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    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.
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    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.
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    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.
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    Artificial neural network model for prediction of rock properties from sound level produced during drilling
    (2013) Rajesh Kumar, B.; Vardhan, H.; Govindaraj, M.; Saraswathi, P.S.
    In many rock engineering applications such as foundations, slopes and tunnels, the intact rock properties are not actually determined by laboratory tests, due to the requirements of high quality core samples and sophisticated test equipments. Thus, predicting the rock properties by using empirical equations has been an attractive research topic relating to rock engineering practice for many years. Soft computing techniques are now being used as alternative statistical tools. In this study, artificial neural network models were developed to predict the rock properties of the intact rock, by using sound level produced during rock drilling. A database of 832 datasets, including drill bit diameter, drill bit speed, penetration rate of the drill bit and equivalent sound level (Leq) produced during drilling for input parameters, and 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) of intact rock for output, was established. The constructed models were checked using various prediction performance indices. Goodness of the fit measures revealed that recommended ANN model fitted the data as accurately as experimental results, indicating the usefulness of artificial neural networks in predicting rock properties. © 2013 Copyright Taylor and Francis Group, LLC.
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    Multiple regression model for prediction of rock properties using acoustic frequency during core drilling operations
    (Taylor and Francis Ltd., 2020) Vijaya Kumar, V.; Vardhan, H.; Murthy, C.S.N.
    The primary purpose of this study is the quantification of rock properties uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), density and abrasivity, using sound signal dominant frequencies produced during diamond core drilling operations. Rock drilling operations were performed on seven different types of rock samples, using a computer numerical control (CNC) drilling machine. Using the multiple regression analysis, satisfactory mathematical equations were developed for various physico-mechanical rock properties, as well as dominant frequencies of the sound level were generated during diamond core drilling operations. The developed prediction models demonstrated a good regression coefficient between the rock properties and dominant frequencies i.e. the R2 values are 82.50%, 78.41%, 79.40%, and 93.24% for UCS, BTS, density and abrasivity, respectively. The performances indices are: (i) root-mean-square error (RMSE) are 0.102754, 1.241652, 0.396727, and 0.697889 for UCS, BTS, density and abrasivity, respectively; (ii) values account for (VAF) is 82.50008%, 78.41137%, 79.40137%, and 93.23596% for UCS, BTS, density and abrasivity, respectively. Presently, it is in the early stages of development towards the prediction of rock properties using dominant frequencies with the help of audio signal processing in the rock drilling operation. The developed prediction models can be utilised at the early stages of mining and civil engineering projects, for the quantification of rock properties using sound signal dominant frequencies. © 2019 Informa UK Limited, trading as Taylor & Francis Group.
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    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.
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    Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning
    (Nature Research, 2025) Mangalpady, M.; Vardhan, H.; Tripathi, A.K.; Parida, S.; RajaSekhar Reddy, N.V.; Sivalingam, K.M.; Yingqiu, L.; Elumalai, P.V.
    Monitoring and predicting ground vibration levels during blasting operations is essential to safeguard mining sites and surrounding communities. This study introduces an IoT-based ground vibration monitoring device specifically designed for limestone mining operations, combined with machine learning algorithms to predict ground vibration intensity. The primary aim is to provide an efficient predictive tool for anticipating hazardous vibration levels, enabling proactive safety measures. A comparative analysis with the industry-standard Minimate Blaster indicates high accuracy of the IoT device, with percentage errors as low as 0.803% across multiple blasts. The study also employed Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Random Forest (RF) algorithms to predict Peak Particle Velocity (PPV) values. Among these, the Random Forest model outperformed the others, achieving an R2 score of 0.92, Mean Absolute Error (MAE) of 0.21, and Root Mean Squared Error (RMSE) of 0.31. These findings underscore the reliability and predictive accuracy of the IoT-integrated Random Forest model, suggesting that it can significantly contribute to enhancing safety and operational efficiency in mining. The research highlights the potential of IoT and machine learning technologies to transform ground vibration monitoring, promoting safer and more sustainable mining practices. © The Author(s) 2025.