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Item Worldwide, increasingly stringent regulations are coming into force, limiting the exposure of workers to industrial noise. Industrial noise and its consequences are thus growing in importance to employers, local and central government officials, trade unions, occupational hygienists and physicians and insurers. India is not an exception to this. The mining industry in India is facing serious problems over noise due to increasing demand for minerals for which large capacity machines are being deployed producing high noise levels. To know the status and to control the noise, the S&T Department of the Ministry of Coal, Government of India sponsored a research project in the area of noise pollution and its control for opencast projects. To start with, a detailed literature survey was carried out in the area of noise pollution and its control in the mining industry, embracing equipment like from Heavy Earth Moving Machinery (HEMM), compressors, workshops, pneumatic drills, processing plants etc, to know the quantum of work done in and worldwide. The various aspects studied In this project were the daily noise dose and/or noise exposure level of the operators of various types of heavy earth moving machinery and its assessment, noise characteristics at different operating conditions of the machine, analysis of noise coming out from different parts of the machine, analysis of noise at different distances from the machine for different frequency components and the most important one i.e., impact of periodic maintenance on the noise characteristics of machines and to find out with which maintenance schedule there is maximum fluctuation in the noise level and to evolve a technique for attenuating the noise generated from these machines as well as to reduce the operator's exposure to high noise levels. This paper highlights the results of this research project.(Multi-Science Publishing Co. Ltd, Noise analysis of heavy earth moving machinery deployed in opencast mines and development of suitable maintenance guidelines for its attenuation - Part 3) Vardhan, H.; Rao, Y.V.; Karmakar, N.C.2004Item Worldwide, increasingly stringent regulations are coming into force, limiting the exposure of workers to industrial noise. Industrial noise and its consequences is thus growing in importance to employers, local and central government officials, trade unions, occupational hygienists and physicians and insurers. India is not an exception for this. The mining industry in India is facing serious problem of noise due to increasing demand for minerals for which large capacity machines are being deployed producing high noise levels. To know the status and to control the noise, the S&T Department of the Ministry of Coal, Government of India sponsored a research project in the area of noise pollution and its control for opencast projects. To start with, a detailed literature survey was carried out in the area of noise pollution and its control in the mining industry, embracing equipment like from Heavy Earth Moving Machinery (HEMM), compressors, workshops, pneumatic drills, processing plants etc., to know the quantum of work done in India and worldwide. The various aspects studied in this project were the daily noise dose and/or noise exposure level of the operators of various types of heavy earth moving machinery and its assessment, noise characteristics at different operating conditions of the machine, analysis of noise coming out from different parts of the machine, analysis of noise at different distances from the machine for different frequency components and the most important one i e, impact of periodic maintenance on the noise characteristics of machines and to find out with which maintenance schedule there is maximum fluctuation in the noise level and to evolve a technique for attenuating the noise generated from these machines as well as to reduce the operator's exposure to high noise levels. This paper highlights the results of this research project.(Multi-Science Publishing Co. Ltd, Noise analysis of heavy earth moving machinery deployed in opencast mines and development of suitable maintenance guidelines for its attenuation - Part 1) Vardhan, H.; Rao, Y.V.; Karmakar, N.C.2004Item Multiple seam mining: A critical review(2006) Khare, S.; Rao, Y.V.; Murthy, Ch.S.N.; Vardhan, H.Multiple seam mining implies simultaneous working of more than one seam. The major problem encountered in this type of mining are related to ground control. These ground control problems are mainly due to the pillar load transfer. The pillar load transfer mainly depends on the physical characteristics of the parting material between the seams and also its thickness. There are various parameters affecting the multiple seam working. Broadly these parameters are classified into two i.e. fixed parameters and mine design parameters.' Fixed parameters are seam thickness, depth of seam, parting thickness, physical characteristics of the parting, geology of the seam, etc. Mine design parameters are sequence of working of the seam, method of working, pillar size, type of supports used, etc. Most of the coal reserve in India is locked up in multi-seams. This paper discusses the various research work carried out in India as well as abroad, related to ground control problems associated with multi-seam mining.Item Acoustic fingerprinting for rock identification during drilling(Inderscience Publishers, 2014) Shreedharan, S.; Hegde, C.; Sharma, S.; Vardhan, H.During the process of mining, it is imperative to know the type and properties of the rocks being handled. The current technology for this involves core drilling, and subsequently subjecting the drilled cores to various tests in the laboratory, to identify the rocks and establish their properties. In many cases, obtaining a sample may be cumbersome and/or non-profitable. This paper presents a novel method to monitor and evaluate the sounds produced as undesirable by-products, at the drill-bit and rock interface, to predict the type of rock being drilled. A rotary drill was fabricated in the laboratory and vertical drilling was carried out on cubical rock samples, keeping various drilling parameters constant. The results obtained are promising and reinforce that it may be possible to extend the proposed methodology in the field as well, with appropriate modifications. This method may be extrapolated further in the estimation of rock properties as well. Copyright © 2014 Inderscience Enterprises Ltd.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.