Faculty Publications
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Item Estimation of Strength Properties of Some Rocks using Ball Mill Grinding Characteristics(World Researchers Associations, 2025) Sahas, S.V.; Bijay, K.M.; Chandar, K.R.The strength properties of rocks namely uniaxial compressive strength and tensile strength are important in design and stability evaluation of various mining, geotechnical engineering and other rock engineering projects. Accurate determination of these properties relies on high-quality samples, but challenges like sample availability, preparation of sample, cost and time constraints have led to an increasing reliance on computational methods for prediction. In this context, an indirect approach is proposed for predicting rock strength properties, specifically the uniaxial compressive strength (UCS) and tensile strength (TS), using grinding characteristics of ball mill, an unconventional yet indirect approach. A predictive modelling using multivariate regression is carried out to estimate the relationship between UCS, TS and the grinding characteristics of ball mill. The developed models demonstrated high accuracy with R² values of 0.93 for UCS and 0.96 for TS. Performance evaluation metrics showed an RMSE of 6.03 MPa and a VAF of 93.45% for UCS and an RMSE of 0.99 MPa and a VAF of 96.47% for TS. The validation was performed using experimental UCS and TS values of basalt rocks along with ball mill grinding test data. The error analysis revealed that UCS prediction error ranged from 5.1% to 11.61% while TS prediction error varied between 4.26% and 16.39%. © 2025, World Researchers Associations. All rights reserved.Item Analysis of Accidents Data of Contractual Workers in Open Cast Metal Mines(World Researchers Associations, 2025) Manohar, M.; Bijay, K.M.This research delves into the intricate dynamics of risk factors contributing to injuries in open cast metal mines where a multitude of personal and impersonal elements converge to shape the safety landscape. Drawing insights from a comprehensive literature review, risk factors considered in the study are skill level of workers, the role of mine officials, attitudes toward safety and the involvement of contractors. The one-year contractual workers’ accident data which includes offsite and onsite injuries was considered for the analysis. The analysis of the provided data on on-site and off-site injuries reveals distinct patterns and trends throughout the month. The comparison of one year data set indicates that overall off-site injuries are more prevalent. Ultimately, this analysis contributes valuable insights for enhancing overall safety measures and mitigating the incidence of injuries. © 2025, World Researchers Associations. All rights reserved.Item A Hybrid Random Forest optimized with the Dolphin Swarm Algorithm for predicting P-Wave Velocity of Sedimentary Rocks using Ball Mill Grinding Characteristics(World Researchers Associations, 2025) Sahas, S.V.; Bijay, K.M.; Chandar, K.R.Rock properties play a crucial role in mining, geotechnical engineering and various engineering projects. P-wave velocity helps in determining the quality and stability of rock masses, essential for tunnel excavation, slope stability and mining operations. P-wave velocity also provides critical input for designing foundations for dams, bridges and other rock structures. Accurate determination of P-wave velocity relies on high-quality samples. However, challenges such as preparation, cost and time constraints have prompted a growing reliance on computational methods for its prediction. Previous investigations predominantly leaned on laboratory-based tests and indirect methodologies for predicting rock properties including P-wave velocity. In contrast, this study introduces an innovative technique for predicting wave velocity (Vp) of sedimentary rocks, particularly limestone using ball mill grinding characteristics throughout the grinding procedure, an unconventional yet effective approach. A hybrid random forest model optimized with dolphin swarm algorithm was developed to predict Vp from grinding characteristics. The performance of the model in training and testing phases was assessed based on determination coefficients (R2), root mean-squared error (RMSE) and variance account for (VAF) which are 0.984, 96.204 m/s and 98.25% in training and 0.973, 102.32 m/s and 97.63% in testing phase respectively. © 2025, World Researchers Associations. All rights reserved.