Faculty Publications
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Item Portable low cost drill set-up for estimating rock properties(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2014) Masood; Vardhan, H.; Mangalpady, M.The mechanical strength of rock is one of the most important factors of concern to engineers involved in mining operations. Information about rock strength is used in rock excavation planning and design operations in civil and mining engineering. Drilling is widely carried out in hard rock’s for blasting the rock mass so that the blasted material can be easily loaded by the excavators. The drillability of rock depends on many factors including rock properties. Whereas properties such as compressive strength, porosity, density etc. are uncontrollable parameters during drilling process. A number of studies have been reported recently on the application of sound level, which have been concentrated on using either CNC or jack hammer machine for drilling purpose. It is worth mentioning that neither CNC machine nor jack hammer drill set-up is the normal way of drilling in rock, nor in mining, civil or any other operations, not even in working with rock in installation of countertops. Therefore, it is difficult to exactly say whether the noise generated during drilling is only from the rock drilling or from the drilling unit itself. In view of the above, it is important to fabricate a new drilling set-up which is a silent unit in itself. Such unit when used for drilling purpose will clearly indicate the change in sound level produced with different rock properties. © 2014 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.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 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 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.