Faculty Publications
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Item Miners’ return to work following injuries in coal mines; Powrót do pracy górników poszkodowanych w wypadkach w kopalni w?gla(Nofer Institute of Occupational Medicine ul. sw. Teresy od Dzieciatka Jezus 8 Lodz 91-348, 2016) Bhattacherjee, A.; Kunar, B.M.Background: The occupational injuries in mines are common and result in severe socio-economical consequences. Earlier studies have revealed the role of multiple factors such as demographic factors, behavioral factors, health-related factors, working environment, and working conditions for mine injuries. However, there is a dearth of information about the role of some of these factors in delayed return to work (RTW) following a miner’s injury. These factors may likely include personal characteristics of injured persons and his or her family, the injured person’s social and economic status, and job characteristics. This study was conducted to assess the role of some of these factors for the return to work following coal miners’ injuries. Material and Methods: A study was conducted for 109 injured workers from an underground coal mine in the years 2000-2009. A questionnaire, which was completed by the personnel interviews, included among others age, height, weight, seniority, alcohol consumption, sleeping duration, presence of diseases, job stress, job satisfaction, and injury type. The data was analyzed using the Kaplan-Meier estimates and the Cox proportional hazard model. Results: According to Kaplan-Meier estimate it was revealed that a lower number of dependents, longer sleep duration, no job stress, no disease, no alcohol addiction, and higher monthly income have a great impact on early return to work after injury. The Cox regression analysis revealed that the significant risk factors which influenced miners’ return to work included presence of disease, job satisfaction and injury type. Conclusions: The mine management should pay attention to significant risk factors for injuries in order to develop effective preventive measures. © 2016, Nofer Institute of Occupational Medicine. 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 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 Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques(Nature Research, 2025) Swamy, S.V.; Kunar, B.M.; Chandar, K.R.; Alwetaishi, M.; Shashikumar, S.; Reddy, S.Uniaxial Compressive Strength (UCS) is a fundamental parameter in rock engineering, governing the stability of foundations, slopes, and underground structures. Traditional UCS determination relies on laboratory tests, but these face challenges such as high-quality core sampling, sample preparation difficulties, high costs, and time constraints. These limitations have driven the adoption of indirect approaches for UCS prediction. This study introduces a novel indirect method for predicting uniaxial compressive strength, harnessing the grinding characteristics of a ball mill as predictive variables through supervised machine learning techniques. The correlation between grinding characteristics and UCS was examined to determine whether a linear relationship exists between them. A hybrid support vector machine-recursive feature elimination (SVM-RFE) algorithm is applied to identify the critical grinding parameters influencing UCS. Four supervised machine learning models viz., Multiple Linear Regression (MLR), k-Nearest Neighbor Regression (k-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR) were developed for UCS prediction, with hyperparameter optimization performed using RandomisedSearchCV technique. The Random Forest model outperformed others as the best prediction model, achieving a coefficient of determination (R²) of 0.95, followed by SVR (R² = 0.87), k-NNR (R² = 0.82), and MLR (R² = 0.758). Model robustness was further assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF). Internal validation by means of K-fold cross validation and external validation with independent datasets confirmed generalization capability, showing an average prediction error of ± 10%. The findings demonstrate that combining grinding characteristics with machine learning offers an accurate, cost-effective alternative to conventional UCS testing, with significant practical applications in rock engineering. © The Author(s) 2025.