Faculty Publications
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Item Risk factors associated with work-related musculoskeletal disorders among dumper operators: A machine learning approach(Elsevier B.V., 2023) Kar, M.B.; Mangalpady, M.; Kunar, B.M.Aims: This study aimed to determine the risk factors associated with work-related musculoskeletal disorders (WRMSDs) among dumper operators working in Indian iron ore mines. Methods: A total of 246 dumper truck operators meeting inclusion and exclusion criteria were chosen for data collection. A self-report custom and the standard Nordic questionnaire were used for collecting data about risk factors and WRMSDs. The data were pre-processed and analyzed using machine learning (ML) algorithms (such as logistic regression ( LR), support vector machines (SVM), decision trees (DT), gradient boosting machine (GBM) and random forest (RF)). Results: RF model was found to outperform the other algorithms with high accuracy (0.71), precision (0.75), recall (0.78), F1 score (0.76), and area under the receiver operating characteristic curve (0.82). The mean rank of the risk factors showed that age is the most critical parameter, followed by awkward posture, experience in mines, job demand, alcohol consumption, smoking cigarettes, work design, and marriage status. Conclusion: Overall, the study provides valuable insights into the risk factors associated with WRMSDs among dumper operators and suggests that measures should be taken to address these risk factors to prevent WRMSDs in the dumper operator population. © 2023 The Author(s)Item Structural equation modelling of work related musculoskeletal disorders among dumper operators(Nature Research, 2023) Kar, M.B.; Mangalpady, M.; Kunar, B.M.The aim of this study is to investigate the impact of personal factors, habitual factors, and work-related factors on work-related musculoskeletal disorders (WRMSDs) among dumper operators. In total, 248 dumper operators working in an iron ore mine were considered for this study. A questionnaire was developed and administered to collect dumper operators' personal, habitual, and work-related data. The reliability of the questionnaire was cross-checked by Cronbach alpha and the test–retest method. The values of Cronbach alpha for all latent variables were above 0.7, and the correlation coefficient of the questionnaire items at Time 1 and Time 2 was above 0.82. After verifying the validity (i.e., convergent and divergent) of the questionnaire data, the relationship between the factors under consideration was examined by structural equation modeling (SEM). The SEM demonstrated a moderate fit, with χ2df value of 1.386, comparative fit index (CFI) of 0.86, goodness-of-fit index (GFI) of 0.72, adjusted goodness-of-fit index (AGFI) of 0.69, Tucker-Lewis Index (TLI) of 0.83, normed fit index (NFI) of 0.71 and root mean square error of approximation (RMSEA) of 0.051. The SEM analysis revealed a positive relationship between WRMSDs and personal factors (with path coefficient = 0.313 and p < 0.05) as well as work-related factors (with path coefficient = 0.296 and p < 0.05). However, the relationship between WRMSDs and habitual factors was not statistically significant (with path coefficient = 0.142 and p > 0.05). Overall, this study provides valuable insights into the factors that influence the prevalence of WRMSDs among dumper operators. The findings highlight the significance of personal and work-related factors by which one can make a positive decision to prevent and reduce the incidence of WRMSDs among dumper operators. © 2023, Springer Nature Limited.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 Durability characteristics of geopolymer concrete produced using gold ore tailings along with recycled coarse aggregates(Nature Research, 2025) Lokesha, E.B.; Mangalpady, M.; Kumar Reddy, S.K.The Gold Ore Tailings (GOTs) are one of the major waste materials in the mining sector. The disposal of these tailings could be a problem for human health and a major environmental concern for several years. In this research work, the GOTs were used as an alternative material to the River Sand (RS) in the production of Geopolymer Concrete (GPC). Thus developed GPC samples were tested for its durability characteristics, such as resistance to sulphates and chlorides. The sulphate attack test was conducted by immersing the Conventional Concrete (CC) and GPC samples in 5% Magnesium Sulphate (MgSO4) solution for various curing periods. In this test, the GPC samples showed a reduction in compressive strength and weight, which is slightly more when compared to CC samples. The Rapid Chloride Penetration Test (RCPT) was also conducted to know the chloride ion penetration in which GPC samples exhibited less chloride penetration when compared to CC samples. Further, the Toxic Characteristic Leaching Procedure (TCLP) analysis showed that the GOTs have very high concentrations of hazardous metals. However, the concentration of Cyanide (CN?) was minimal in GOTs. In this regard, geopolymerization would be a better method for immobilizing the hazardous metals in GOTs. © The Author(s) 2025.Item 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.