Faculty Publications
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Item Evaluation of Whole Body Vibration of Heavy Earth Moving Machinery Operators(Springer Nature, 2020) Jeripotula, S.K.; Mangalpady, A.; Raj, G.R.Operators of Heavy Earth Moving Machinery (HEMM) performing routine tasks in surface mines are highly vulnerable to whole body vibration (WBV) due to their continuous exposure to vibration. In the present study seventeen types of machinery were considered for the evaluation of the operator’s exposure to WBV. The measurements were made by placing the triaxial seat pad accelerometer on operator’s seat-surface as well as at the seat-back. Among these machinery one shovel, two front-end loaders, three drills, one grader and one water sprinkler were found to have RMS values in the severe zone as per ISO2631-1:1997 standards for seat-surface measurements. Similarly, for the seat-back measurements, one front-end loader, two drills, one grader and one water sprinkler were experienced the highest RMS value. For both seat-surface and seat-back measurements, Z-axis (i.e. vertical direction) was found to be a prominent axis for most of the machinery. © 2020, Springer Nature Switzerland AG.Item Evaluation of Whole-Body Vibration (WBV) of Dozer Operators Based on Job Cycle(Springer, 2019) Jeripotula, S.K.; Mangalpady, A.; Raj, G.R.Dozer operators are frequently exposed to high levels of occupational vibration. So far, no study reported component wise evaluation of dozer cycle of operation. In the present study, WBV data were collected by placing the trial accelerometer at operator’s seat-surface and at seat-back. Frequency-weighted root mean square (RMS), vibration dose value (VDV) and crest factor were collected for each dozer for two phases’ forward motion and return motion. All the dozers under study were found to be in severe zone with respect to measured RMS, during forward motion and return motion, irrespective of type of measurements (i.e., seat-surface and seat-back). As per VDV, out of eight dozers three dozers were found to be in caution zone during forward motion and three in return motion. According to EU Directive 2002 (as per RMS), all the dozers under study have reported exposure action value above 0.5 m/s2. Further, out of eight dozers, four dozers have shown exposure limit value above 1.15 m/s2 for seat-surface measurements and three dozers for seat-back measurements. Vibration mitigation strategies should be adapted not just based on intensity of vibration but also with respect to dominant axis of vibration. Considering the severe health risk due to the translational vibration (i.e., in x-direction), the vibration risk in the forward x-direction can be reduced by using seat belt; similarly in rear x-direction it can be attenuated by placing lumber-assisted back rest. © 2019, The Institution of Engineers (India).Item Assessment of Exposure to Whole-Body Vibration of Dozer Operators Based on Postural Variability(Springer, 2020) Jeripotula, S.K.; Mangalpady, A.; Raj, G.R.The main aim of this work is to evaluate whole-body vibration (WBV) of dozer operators based on three sitting postures (i.e., with 15° lean forward inclination posture, vertically erect posture with no inclination, and with 15° lean backward inclination posture) in Indian surface coal mines. A seat pad tri-axial accelerometer was used to collect WBV data from six dozer operators for three different sitting postures. Results showed that except for Dozer-1, 2, 4, and 5 operators during lean forward sitting posture and Dozer-4 operator during vertical erected posture, no other dozer operators have exceeded an exposure limit value (ELV) of 1.15 m/s2 in any of the considered sitting postures. Similarly, the vibration dose value (VDV) based on exposure action value (EAV) of 9.1 m/s1.75 has surpassed for all the dozers. But no dozer operator has exceeded an exposure limit value of 21 m/s1.75. The outcome of the study infers that based on “above health guidance caution zone (HGCZ)” for daily vibration exposure, i.e., A(8) measurements, for the operator sitting in lean backward postures the vibration amplification was reduced by 32.89% less compared with lean forward posture and 16.23% less when compared with vertically erected posture. Similarly, based on VDV(8), the exposure to vibration for the lean backward posture was reduced by 33.34% when compared with lean forward posture and 17.11% less when compared with vertically erected posture. Based on the above observation, it is concluded that lean back inclination with a trunk flexion of 15° is a favorable sitting posture, as it exposes the dozer operators to minimum vibration. © 2020, Society for Mining, Metallurgy & Exploration Inc.Item Human-in-the-Loop Data Analytics for Classifying Fatal Mining Accident Causes Using Natural Language Processing and Machine Learning Techniques(Springer Science and Business Media Deutschland GmbH, 2025) Sharma, A.; Kumar, A.; Vardhan, H.; Mangalpady, A.; Mandal, B.B.; Senapati, A.; Akhil, A.; Saini, S.Mining remains one of the most hazardous industries globally, marked by frequent fatalities resulting from complex operational risks. While accident investigation reports hold valuable insights for improving safety practices, the manual coding of fatality narratives remains labor-intensive, inconsistent, and impractical for large datasets. Although natural language processing (NLP) and machine learning (ML) techniques have gained traction for automating the analysis of safety narratives in other high-risk industries, their application to mining accident data, particularly within the Indian context, remains limited. Addressing this gap, the present study proposes a ML framework for the semi-automated classification of fatal accident causes from unstructured text narratives reported by the Directorate General of Mines Safety (DGMS) between 2016 and 2022. A total of 401 fatal accident descriptions were pre-processed and vectorized using Bag-of-Words, TF-IDF, and Word2Vec techniques, followed by model evaluation across multiple algorithms. A semi-automated classification scheme was developed to balance efficiency with expert oversight, where high-confidence predictions were assigned automatically and uncertain cases were flagged for manual review. Logistic regression combined with TF-IDF unigram features achieved the highest performance, with an F1 score of 0.78 and an accuracy of 0.81. Overall, the developed framework successfully auto-coded 68.75% of cases with 94% accuracy, 0.93 recall, and 0.91 precision. Word cloud visualizations were also employed to capture dominant words associated with different cause categories. The proposed framework offers a practical and operationally feasible solution for assigning fatality causes in the mining sector, contributing to active safety management, surveillance, and policy formulation. © Society for Mining, Metallurgy & Exploration Inc. 2025.