Faculty Publications
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Item Effect of Cement and Quarry Dust on Shear Strength and Hydraulic Characteristics of Lithomargic Clay(2012) Nayak, S.; Sarvade, P.G.The lithomargic clay constitutes an important group of residual soils existing under lateritic soils. This soil is found on the western and eastern coasts of India over large areas. This soil is a problematic one and is very sensitive to water and loses a greater part of its strength when becomes saturated. These high silt deposits have invited many problems such as slope failures, foundation failures, embankment failures, uneven settlements etc. In this investigation an attempt is made to study the effect of cement and quarry dust on shear strength and hydraulic characteristics of the lithomargic clay after the stabilization. Microfabric and mineralogical studies were carried out to find out the reason for the strength development of the stabilized soil using SEM and XRD analysis. The results indicated that there is an improvement in the properties of the lithomargic clay with the addition of cement and quarry dust. The XRD results indicated the formation of CSH and CAH, which are responsible for strength development in the stabilized soil. © 2011 Springer Science+Business Media B.V.Item Chemical characterization and source apportionment of ambient PM10 in Hubli-Dharwad region, Karnataka, India(World Research Association, 2021) Navalgund, N.; Keshava, J.; Krishna, K.; Srinikethan, S.; Sampagaon, N.; Manoj, K.; Aishwaraya, S.In India, particulate matter (PM10) shows very strong persistence and very high levels in most of the tier-II cities along with metropolitan cities and other cities of the world. The present work was to study air pollution (PM10) in Hubli-Dharwad, a tier-II city of Karnataka, India. The mean mass concentration for PM10 varied from 260-410 ppm, substantially higher than guidelines of CPCB. Seasonal variations of these pollutants indicated that higher concentrations of pollutants were observed in summer than in winter seasons with air quality index (AQI) as 211 in summer. The source apportionment study using positive matrix factorization (PMF5) indicated the presence of heavy metals in the atmosphere. Out of 4 identified factors, motor vehicles contributed vastly (38.8%), dust (16.4%), industrial emission (19.8%) and biomass burning (25.0%). This study has found that the source apportionment has distinct regional and seasonal characteristics. Such studies are essential for the Government to make region specific control strategies for particulate pollution in India. © 2021 World Research Association. All rights reserved.Item Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques(Nature Research, 2024) Tripathi, A.K.; Mangalpady, M.; Parida, S.; Durgesh Nandan, D.; Elumalai, P.V.; Prakash, E.; Joshua Ramesh Lalvani, J.S.C.; Koppula, K.S.The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.5, PM 4.0, and PM 10.0. The novelty of this work lies in the integration of IoT technology for real-time measurement and the application of machine learning (ML) techniques for accurate prediction based on recorded dust pollutants data. The study's findings indicate that PM 1.0 pollutants exhibited the highest concentration in the atmosphere of the ball clay surface mine sites, with the stockyard site registering the maximum levels of PM pollutants (28.45 µg/m3, 27.89 µg/m3, 26.17 µg/m3, and 27.24 µg/m3, respectively) due to the dry nature of clay materials. Additionally, the research establishes four ML models—Decision Tree (DT), Gradient Boosting Regression (GBR), Random Forest (RF), and Linear Regression (LR)—for predicting PM pollutant concentrations. Notably, Random Forest demonstrates superior performance with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) at 1.079 and 1.497, respectively. This comprehensive solution, combining IoT-based monitoring and ML-based prediction, contributes to sustainable mining practices, safeguarding worker well-being, and preserving the environment. © The Author(s) 2024.Item Investigation of Dust Emission in Limestone Mines and its Statistical Prediction using Supervised Machine Learning (Regression) Modelling(World Researchers Associations, 2025) Rajib, P.; Harsha, V.; Shanmugam, S.B.; Harish, H.; Amrites, S.In India, the fugitive dust emissions in the processing plant and mining area of limestone mines are very high. The dust emission of (particulate matter) PM10 and PM2.5 forms an unsafe working environment for workers in processing plant areas and mining areas. The excessive emission of PM10 and PM2.5 will cause lung-related diseases to the workers and the people existing in the adjacent areas of the mine. The dust emission majorly causes air pollution to occur due to the distribution of particulate matter in the work area. This study majorly investigates the dust emission levels of PM10 and PM2.5 in the limestone mine of Kadapa, Andra Prasad, India. The investigation on the dust emission of PM10 and PM2.5 was carried out as per the guidelines of DGMS and MoEF and CC guidelines, with a specific focus on PM10 and PM2.5 particulate matter. From the study, it was clear that the dust emission levels of PM10 and PM2.5 in the mine area and some parts of the processing area were below the permissible limit of 1200 ?g/m³ as per the National Ambient Air Quality Standards (NAAQS, 2009). It was also found that the dust emission levels of PM10 and PM2.5 in the crushing and screening area of the processing plant were above the permissible limit of 1200 ?g/m³. Further the statistical prediction model was developed using linear, quadratic and cubic supervised machine learning (regression) modelling. The results indicated that the cubic regression model will provide the accurate prediction of fugitive dust emission with lower error and standard deviation. © 2025, World Researchers Associations. All rights reserved.Item Experimentation and Statistical Prediction of Dust Emission in Iron Ore Mines using Supervised Machine Learning (Regression) Modelling(World Researchers Associations, 2025) Rajib, P.; Harsha, V.; Senapati, A.; Sahas, S.V.In India, the mine area and the processing plant of materials such as iron ore and coal will cause dust emissions. The fugitive dust emission creates a hazardous working environment for the workers. Dust emissions will cause pulmonary-related diseases to the workers and also to the people living in nearby areas of the mine. Environmental effects such as air pollution occur due to the dispersion of particulate matter over the permissible limit in the processing area. This study evaluates dust emission levels and air quality control measures in an iron ore mine (A), Karnataka, India. Fugitive and workplace dust sampling was conducted following DGMS and MoEF and CC guidelines, with a specific focus on PM10 and PM2.5 particulate matter. Measurements revealed that dust concentrations in several mining areas exceeded the permissible limit of 1200 ?g/m³ as per the National Ambient Air Quality Standards (NAAQS, 2009). To analyze and predict these concentrations, supervised machine learning (regression) modeling including linear, polynomial (order 2) and polynomial (order 2) models, was applied. The results indicated that a third-order polynomial regression model provided the best fit for predicting dust concentrations, demonstrating lower error. The study emphasizes the necessity of more robust dust suppression measures including installing a dry fog dust suppression system, to guarantee safe working conditions and adherence to environmental regulations, even in the face of efforts to reduce dust exposure. © 2025, World Researchers Associations. All rights reserved.