Faculty Publications
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Publications by NITK Faculty
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Item 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.Item Reshaping berm breakwaters: A physical model study(National Institute of Science Communication and Policy Research, 2018) Janardhan, P.; Rao, S.; Shirlal, K.G.In the present study, the structural stability of statically stable reshaping berm breakwater for different wave parameters and armour weights were verified by physical model study carried out at NITK Surathkal Mangalore. Wave run-up and rundown studies were also carried out. The results show that a safe structure can be evolved with reduction in armour weight by up to 25% for all the relative berm position values. The position of berm greater than or equal to 1.3 was found to be good in reducing recession as well as wave run-up. An empirical new berm recession formula was derived for berm recession based on sea state and structural parameters. © 2018, National Institute of Science Communication and Information Resources (NISCAIR). All rights reserved.Item An optimum datasets analysis for monitoring crops using remotely sensed Sentinel-1A SAR data(Taylor and Francis Ltd., 2023) Salma, S.; Keerthana, N.; Dodamani, B.M.To effectively monitor crops, it is necessary to select extremely redundant satellite images and to know the number of acquisitions required for a specific period to analyse cropping patterns, thereby reducing analysis time. In this paper, we have examined an empirical analysis for the optimum dataset (OptD) selection required to monitor the crops. Sentinel-1 dual-polarized SAR datasets were used in this study to illustrate the effectiveness of optimum datasets required for the considered crops (ginger, tobacco, rice, cabbage, and pumpkin). In this work, at first, the entropy and alpha bands were treated as cluster centres for crop decomposition and its scattering mechanism using the cluster-based K-means unsupervised classification technique. The clusters are plotted on the H-α plane to get the H-α plot of dual-polarization SAR data for target decomposition. To understand the dominance of scattering type with crop growth stage, the obtained scattering distribution from the H-alpha plot is scaled to a percentage analysis. Based on qualitative observations of the percent scattering distribution of crop pixels over a h-alpha plot and backscattering coefficient behaviour at different crop growth stages, an empirical approach has been used to select dataset reduction. It has been suggested that the combination of successive repeated data with similar scattering analysis of combined h-alpha plots and backscattering analysis is the best reduced dataset selection for effective crop monitoring. From the analysis, the optimum dataset required for monitoring Ginger (from the flourishing stage), Tobacco, Paddy, Cabbage, and Pumpkin has been identified, and found that the tobacco crop requires fewer datasets, whereas the rice crop requires a greater number of datasets for monitoring. Despite the challenges associated with, p-bias for the crops was achieved at good levels, given that, lowering the datasets to obtain the optimal number without significantly reducing the accuracy of the data. © 2023 Informa UK Limited, trading as Taylor & Francis Group.Item Comparison of model study with field implementation of gravity blind backfilling method to control subsidence induced disaster in abandoned underground coal mines(World Researchers Associations, 2023) Kumar, P.S.; Akhil, A.; Kumar, T.A.Blind hydraulic backfilling technique is used for subsidence control in underground coal mines. A laboratory size model of underground working was developed to understand backfilling process. Observations from model were utilized for backfilling process in one of the underground mines. This study describes the results obtained in the field investigation at an old abandoned waterlogged underground coal mine of Eastern Coalfields Limited (ECL), a subsidiary of Coal India Limited and their verification with the findings obtained in the laboratory scale model study carried out on a model of underground coal mine worked by board and pillar method. The relative influence of slurry concentration and flow rates on the areas of filling from a single inlet borehole has been discussed. The relative spread of sand in different directions has also been measured using a remotely operated underground vehicle mounted camera. The empirical relationships developed under field conditions have been found to be similar to those of laboratory model. © 2023, World Research Association. All rights reserved.Item Ground motion duration predictive models applicable for the Himalayan region(Springer, 2023) Anbazhagan, P.; Motwani, K.Several empirical models for the prediction of ground motion duration were developed across the world, but no model has been generated for the Himalayan region in the past. In this study, an attempt is made to study the duration models developed for different regions and compare them with a reference model developed for the Himalayan region for a wide range of magnitudes. The comparison is performed using the log-likelihood method and aims to identify the best duration prediction models based on the developed by Bajaj and Anbazhagan (2019) for the study region. The data support index values along with the weights of the corresponding models across the different distances and magnitude ranges have also been estimated. The study found that the predictive duration relation given by Lee and Green (2014) for Western North America is suitable for M ≤ 5, while the model developed by Ghanat (2011) is suitable for M > 5 for the Himalayan region. The model developed by Afshari and Stewart (2016) is also very close to the reference model. It is always preferable to have a single duration predictive model for a wide range of magnitude and distance range; hence, there is a need to develop a region-specific duration predictive model for the Himalayan region. © 2023, Indian Academy of Sciences.Item The Impact of Temperature Change on the Firm Performance: Empirical Evidence from the Indian Mining Sector(World Researchers Associations, 2025) Akshaya; Gopalakrishna, B.V.Irrespective of sector, fluctuations in temperature exert a noteworthy impact on the operational dynamics of businesses. The panel data used encompassed 62 publicly listed Indian companies operating in the mining sector over the period from 2011 to 2020 to verify the above in the mining sector. The primary objective is to empirically scrutinize the repercussions of temperature changes on the overall performance of the mining industry in India. Firm-specific variables are kept as control measures in this investigation and a panel quantile regression approach is employed for the analysis. The study reveals that an escalation in the annual average temperature contributes to a decline in the profitability of mining firms. Notably, the observed negative correlation is not consistently uniform across different quantiles. Furthermore, the research establishes that working capital management does not exert a discernible influence on the profitability of mining companies. It is important to note that this empirical analysis is limited to Indian companies exclusively. © 2025, World Researchers Associations. All rights reserved.Item Enhancing infiltration rate predictions with hybrid machine learning and empirical models: addressing challenges in southern India(Springer Science and Business Media Deutschland GmbH, 2025) Ramaswamy, M.V.; Yashas Kumar, H.K.; Reddy, V.J.; Nyamathi, S.J.Despite the success of machine learning (ML) in many disciplines, its application in hydrology, especially in water-scarce regions, faces challenges due to the lack of interpretability and physical consistency. This study addresses these challenges by integrating empirical hydrological models with ML techniques to predict infiltration rates in water-scarce regions of southern India. Using data from 199 observations across 11 sites, including soil characteristics and infiltration measurements, traditional models such as Philip’s, Horton’s, and Kostiakov’s were parameterized and combined with artificial neural networks (ANNs) and the MissForest (MF) algorithm to form hybrid models. The results demonstrate that the hybrid models, particularly those integrating Philip’s model with ANN and multiple predictors, achieved substantial improvements in prediction accuracy, with R2 values ranging from 0.803 to 0.918, root mean-square error (RMSE) from 0.083 to 0.118 cm/min, and Legates’ coefficient of efficiency (LCE) from 0.575 to 0.717 across the target sites. In contrast, empirical models alone at the test sites show lower performance, with R2 ranging from 0.499 to 0.902, RMSE from 0.091 to 0.152 cm/min, and LCE from 0.46 to 0.728, underscoring the limitations of traditional empirical models and the enhancement achieved through ML integration. By leveraging the strengths of empirical models and ML, the hybrid approach improves predictive accuracy and provides a more robust understanding of infiltration dynamics. The hybrid models enable accurate predictions using minimal, readily accessible data, offering a practical solution for water resource management and soil conservation in semi-arid, data-scarce regions. This study demonstrates that blending empirical knowledge with ML algorithms not only improves accuracy but also retains physical interpretability, presenting an innovative solution to hydrological modeling challenges in water-scarce environments. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.Item Climate anomalies and stock market dynamics: Evidence from empirical analysis(Academic Press, 2025) Akshaya, A.; Gopalakrishna, B.V.The longstanding variation in average climate parameters, typically occurring over decades or longer, is known as climate change. The authors examine the impact of climate change anomalies, specifically the changes in temperature and precipitation, on the equity market. This empirical approach utilized monthly long-term time-series data from 1996 to 2024, comprising 348 observations. To test the empirical association between the variables, the study employed the autoregressive distributed lag (ARDL) and Nonlinear ARDL (NARDL) models. The findings of this analysis reveal a significant short-run symmetric effect of temperature changes on market volatility (? = 0.0004, p = 0.010). Increasing temperatures intensify market instability, suggesting that short-term climatic shocks amplify investor uncertainty and risk perception, and heighten market momentum. In contrast, increasing precipitation exhibits a long-term stabilizing effect (? = ?8.91e-06, p = 0.032), indicating that higher rainfall helps mitigate market instability over time. The alternative explanatory data from the World Bank and the GARCH model results are robust to the primary outcome. The study's outcomes provide valuable insights for regulatory bodies' climate disclosure policies and highlight the importance of proactive hazard management, particularly for investors in emerging markets and vulnerable sectors that are more susceptible to climate-driven volatility. © 2025 Elsevier Ltd