Browsing by Author "Mahanta, M."

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    Role of Individual and Occupational Factors on Injuries among Contractual Workers in Surface Mines-Machine Learning Approach
    (Informatics Publishing Limited, 2025) Mahanta, M.; Kunar, B.M.
    This study integrates data analysis and machine learning techniques to investigate workplace injury prediction and mitigation in surface mining operations. Injury data (from March 2023 to Aug 2024) was collected from mechanised limestone mines in Raipur, Chhattisgarh, encompassing 446 workers' demographic details, health data and workplace conditions. The methodology involved exploratory data analysis, linear and non-linear regression models and advanced machine learning algorithms such as XG Boost, random forest, decision tree, and Bayesian ridge regression. risk factors namely skill level (semi-skilled), age (mean 43 years), experiences (mean 14 years), designation, qualifications, health status, work environment, and safety culture were analysed to predict injury occurrences. The results reveal that near misses and minor incidents are significant early indicators (T-value 1.35) of severe injuries, emphasising proactive safety measures. Among the models evaluated, XG Boost well performed with a training R-squared of 0.95 and test R-squared of 0.43, demonstrating its superior ability to generalise and capture complex relationships in the data. The analysis found that injuries are moderately common for contractual workers (mean: 0.90 per instance), with significant variability in near misses (mean: 8.05). Standardised working conditions, such as uniform duty hours (8 hrs.) and leave entitlements (30 days/year), contribute to the occurrence of injuries (t-value 1.35). Proactive measures to be taken in addressing factors like worker skill levels, availability of safety features and positive attitudes toward safety, are crucial for mitigating injuries at workplaces. Major Findings: From the study it can be concluded that the factors namely fire (T-value=5.64), experience (T-value=4.69), skill level (T-value=5.08), positive attitude towards safety (T-value=3.68), medical facilities (T-value=4.35), working conditions of the site (T-value=5.23) and available equipment with safety features (T-value=4.55) are the significant factors that contribute to the occurrence of the injuries among contractual workers in surface mines. The machine learning algorithm models namely extreme gradient boost (XG Boost) performed best for the prediction of the injuries data consider for the study with training data set, (R-squared 0.95) and testing data set (R-squared 0.43). © 2025, Informatics Publishing Limited. All rights reserved.
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    Study of Electronic Permit to Work System at Limestone Mining and Cement Industry to Control Accident and Injuries
    (Springer Nature, 2025) Mahanta, M.; Kunar, B.M.
    An internationally recognized method of limiting hazardous activity particular high-risk maintenance, is the permit to work system. The permit-to-work is a formal process that grants permission to particular individual to perform particular task during a designated window of time. It is difficult to control large number of worker’s safety database by using traditional paper Permit to Work System. To remove all unforeseen manual mistake ePTW (Electronic Permit to Work) system was introduced by using customized computer network operated webpage by programming language such as HTML (hypertext markup language), CSS (Cascading Style Sheets), JavaScript Language etc. The objective of the study to be make suggestion and implement various measure to improvement in current ePTW system and comparison of accessibility, control, and decreases in accident rate during paper based PTW versus electronic based PTW. History data studied of a Cement industry of India of 40 different locations and found that, on the year 2010, 09 nos. of fatal accidents occurred before implementation of ePTW, now gradually decreases to 02 as on the year 2022 by implementing ePTW. The scope of the study is included both limestone quarry and cement manufacturing process, because most of the workers are working in mining, auxiliary mining activities like electrical sub-station in mines, underground cabling in mines pit, haul road lighting, limestone crushers, mechanical HEMM Maintenance, civil works, IT support services, instrumentation and telecom, pipe line workers, and pump operator. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.