Faculty Publications

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Publications by NITK Faculty

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    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.
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    Oxalic acid optimization for iron-based solid waste conversion into a carbon-sequestering composite building material
    (Elsevier B.V., 2025) M, N.; Palanisamy, T.
    The cement industry significantly contributes to global CO2 emissions, accounting for approximately 164 million metric tonnes annually, while total emissions from all sources reach 37 billion metric tonnes. Concurrently, the iron and steel sector generates substantial waste, producing about 500 kg of waste per tonne of steel. Addressing these environmental challenges is crucial for sustainable development. This study presents a sustainable alternative to traditional cement by developing a novel binder material composed primarily of waste iron. The alternative binder not only avoids CO2 emissions but also absorbs CO2 during carbonation curing, effectively contributing to carbon sequestration. Key parameters, including particle size, oxalic acid dosage, and water-to-binder ratio, were individually tested and analyzed for their impact on compressive strength, leading to the finalization of a 75?m particle size and a 0.2 water-to-binder ratio, which yielded compressive strengths of up to 45 MPa. The wet mix method for oxalic acid incorporation demonstrated superior performance compared to the dry mix approach. Comprehensive analyses, including XRD, FTIR, TGA/DTG, and FESEM, confirmed the enhanced reactivity and performance of the material with finer particles and optimized oxalic acid dosage. By utilizing 80% of waste materials, this alternative binder addresses both waste management and carbon capture, aligning with global sustainability objectives and advancing the development of eco-friendly building materials. © 2024 Elsevier B.V.
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    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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    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.
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    Evaluating Blast Fragmentation: A Comparative Study of Electronic and Shock-Tube Initiation Systems in a Limestone Mine
    (World Researchers Associations, 2025) Vinith Kumar, P.V.; Raina, A.K.; Balamadeswaran, P.; Sambasivam, V.S.; Saravanan, K.; Chandar, K.R.
    Explosive energy is the most widely used method for fragmenting rock masses and mineral deposits in mining operations. The fragmentation achieved during blasting significantly impacts downstream operations including loading, transportation, crushing and processing costs. Among the various factors affecting blast fragmentation, the initiation system plays a crucial role. A study was carried out to compare the performance of electronic detonators with shock-tube detonators, in terms of fragmentation in a limestone mine. Field experiments were conducted to assess the fragment size using digital image analysis technique (DIAT). The results indicated that electronic initiated blasts produced finer average fragment sizes (k50) ranging from 0.31-0.44 m, while as in non-electric shock-tube (NeSt) initiated blasts produced larger fragmentation with k50 values between 0.39-0.51 m. The analysis revealed that average k50 values of blasts initiated with electronic detonator were 20% less than that of non-electric shock tube (NeSt) initiated blasts. This is primarily due to precise delays planned and executed for the rock mass that aid in proper fragmentation. © 2025, World Researchers Associations. All rights reserved.
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    Predicting Burden Rock Velocity in Limestone Mines using Artificial Neural Network Models
    (World Researchers Associations, 2025) Channabassamma, N.; Akhil, A.; Rama, S.V.; Sahas, S.V.; Ranjit, K.
    The prediction of burden rock velocity is crucial in optimizing the efficiency of mining and excavation operations. This study presents a novel approach utilizing Artificial Neural Networks (ANNs) to accurately predict the velocity of burden rocks based on various input parameters such as rock property, geological property and bench properties. A comprehensive dataset was collected from field measurements and laboratory experiments to train the ANN models. The performance of the ANN models such as Multi-layered Perceptron (MLP), Deep Neural Network (DNN), simple MLP and Backpropagation Neural Network (BPNN) was evaluated based on performance metrics R-squared (R)2, Mean Squared Error (MSE) and Mean Absolute Error (MAE). Among the developed ANN models, the BPNN model was found to be the most accurate predictive model for burden rock velocity, as evidenced by metrics R2(0.821), MSE (0.099) and MAE (0.226). The results indicate that the BPNN model effectively captures the complex relationships between the predictors and burden rock velocity. Advanced neural network algorithms such as recurrent neural networks and long short-term memory techniques can be used to improve the accuracy of presented neural network models. © 2025, World Researchers Associations. All rights reserved.
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    Development of an equation to predict blast induced ground vibrations of open cast lime stone mine by using Multiple Linear Regression (MLR)
    (World Researchers Associations, 2025) Appani, R.; Harsha, V.; Subrahmanyam, S.K.V.
    This study focuses on predicting ground vibrations generated by blasting activities in open cast limestone mining by integrating blast design parameters with conventional variables. Blasting is a critical operation for the effective removal of overburden and mineral extraction, but it can lead to significant adverse effects, particularly ground vibrations, which pose challenges for both mining and environmental engineers. Conventional methods for estimating these vibrations typically focus on the distance from the blast site and the maximum charge per delay as key independent variables. Recognizing the substantial impact of blast design on vibration levels, this research employs multiple linear regression analysis to incorporate additional factors such as blast design elements. By developing a more comprehensive predictive model, the study aims to enhance the accuracy of ground vibration forecasts, ultimately contributing to safer and more sustainable mining practices. © 2025, World Researchers Associations. All rights reserved.
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    Experimental and statistical analysis on rate of penetration under the influence of rotational speed for drilling limestone in the open cast mine area
    (World Researchers Associations, 2025) Subrahmanyam, S.K.V.; Harsha, V.; Reddy, B.R.R.; Shanmugam, S.B.; Harish, H.
    In this study, an experimental investigation was carried out to study the rate of penetration for drilling limestone in an open-cast mine. The investigation was also carried out to study the influence of rotational speed. Drilling experiments were carried out with a constant drilling depth of 10m and varying speeds of 40rpm, 45rpm and 50rpm. As the drilling was carried out, the fresh drill bit caused an increase in the rate of drilling penetration. Further, as it reached the optimal level, there was a decrease in the rate of penetration due to the wearing out of the drill bit. Further, the prediction of experimental results was carried out using the regression analysis using linear and polynomial models. The results show that the polynomial model was found to be in close relation with experimental results. © 2025, World Researchers Associations. All rights reserved.
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    Influence of Stiffness Ratio and Powder Factor on Burden Rock Movement in Blasting Operations: A Case Study on Limestone Mines
    (World Researchers Associations, 2025) Channabassamma, N.; Akhil, A.; Rama, S.V.
    In opencast mining, blasting is a critical operation that significantly impacts the efficiency and costeffectiveness of material removal. This study focuses on optimizing the use of explosive energy to move the burden, thereby reducing reliance on mechanical methods. Effective blast design involves strategically utilizing energy within a blast hole, considering factors such as explosive type, quantity, detonation timing and blast hole geometry. Given the rapid nature of blasting, high-speed video cameras are employed to capture the blast progression on a millisecond scale, providing essential data for analyzing blast dynamics. This research evaluates the influence of blast design parameters, specifically the stiffness ratio (the ratio of bench height to the burden) and powder factor (the amount of explosive per unit volume of rock), on the movement of burden rock in a limestone mine. By examining these parameters, the study aims to optimize blast designs to achieve improved fragmentation, reduced fly rock and minimized ground vibrations, ultimately enhancing the efficiency and cost effectiveness of mining operations. © 2025, World Researchers Associations. All rights reserved.