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Development of a predictive model for workers' involvement in workplace accidents in an underground coal mine

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Abstract

Underground mines are dynamic and dangerous. These features of underground coal mines, coupled with the low level of mechanization, have made underground Indian coal mines accident-prone. The mine managers are much stressed about achieving high productivity with safety. It is a fact that human performance is the primary driving force for operating these mines safely, and the work-related factors significantly impact human performance and safety. With the help of demographic data and work-related characteristics, this study seeks to assess the chance of accidents. We achieve this goal using improved work compatibility and a binary logit model. This study employs a step-wise backward elimination technique to develop the logit model with significant work-related factors. When testing the model with available data, we obtained encouraging accuracy. This study employs data envelopment analysis to identify and prioritise work-related factors for the prevention of workplace accidents. Finally, we made some suggestions that may work for enhancing productivity and safety.

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References

  1. Deb M and Chandra Sarkar S 2017 Minerals and Allied Natural Resources and their Sustainable Development: Principles, Perspectives with Emphasis on the Indian Scenario. Springer Geology, Springer Singapore. pp. 489-518

  2. FICCI Mines and Metals Division 2013 Development of India's Mining Sector: The Way Forward. 9-11

  3. Sari M, Sevtap Selçuk A, Karpuz C, Duzgun S 2009 Stochastic modeling of accident risks associated with an underground coal mine in Turkey. Saf. Sci. 47: 78–87

    Article  Google Scholar 

  4. Donoghue A M 2004 Occupational health hazards in mining: an overview. Occup. Med. 54: 283–289

  5. Dash A K, Bhattacharjee R M, Singh C S and Aftab and Sagesh K M R, 2017 A decision can be a disaster: a descriptive analysis of a case study. Int. J. Appl. Environ. Sci. 12: 1803–1820

    Google Scholar 

  6. Dhillon B S 2010 Mine Safety: A Modern Approach. Springer, London, pp 1–11

    Book  Google Scholar 

  7. Gupta S, Kumar P and Gunda Y R 2021 A fuzzy causal relational mapping and rough set-based model for context-specific human error rate estimation. Int. J. Occup. Saf. Ergon. 27: 63–78

    Article  Google Scholar 

  8. Griffith C D and Mahadevan S 2011 Inclusion of fatigue effects in human reliability analysis. Reliab. Eng. Syst. Saf. 96: 1437–1447

    Article  Google Scholar 

  9. DIRECTORATE GENERAL OF MINES SAFETY 2014 Annual Report, Ministry of Labor and Employment, Government of India

  10. Di Pasquale V, Iannone R, Miranda S and Riemma S 2013 An overview of human reliability analysis techniques in manufacturing operations. Oper. Manage. 9: 978–953

    Google Scholar 

  11. Stanton N A 1996 Human factors in nuclear safety. CRC Press. pp. 1-14

  12. Xu J, Anders S, Pruttianan A, France D, Lau N, Adams J A and Weinger M B 2018 Human performance measures for the evaluation of process control human-system interfaces in high-fidelity simulations. Appl. Ergon. 73: 151–165

    Article  Google Scholar 

  13. Basha S A and Maiti J 2017 Assessment of work compatibility across employees’ demographics: a case study. Int. J. Inj. Control Saf. Promot. 24: 106–119

    Article  Google Scholar 

  14. Genaidy A, Karwowski W and A-Rehim A, 2007 The work compatibility improvement framework: preliminary findings of a case study for defining and measuring the human-at-work system. Ergon. 50: 1771–1808

    Article  Google Scholar 

  15. Genaidy A, Karwowski W, Shell R, Khalil A, Tuncel S, Cronin S and Salem S 2005 Work compatibility: An integrated diagnostic tool for evaluating musculoskeletal responses to work and stress outcomes. Int. J. Ind. Ergon. 35: 1109–1131

    Article  Google Scholar 

  16. Genaidy A, Karwowski W and Shoaf C 2002 The fundamentals of work system compatibility theory: an integrated approach to optimization of human performance at work. Theor. Issues Ergon. Sci. 3: 346–368

    Article  Google Scholar 

  17. Farrell M J 1957 The measurement of productive efficiency. J. R. Stat. Soc. A. 120: 253–281

    Article  Google Scholar 

  18. Abdallah S, Genaidy A, Salem O, Karwowski W and Shell R 2004 The concept of work compatibility: An integrated design criterion for improving workplace human performance in manufacturing systems. Hum. Factors Ergon. Manuf. Serv. Ind. 14: 379–402

    Article  Google Scholar 

  19. Salem S, Paez O, Holley M, Tuncel S, Genaidy A and Karwowski W 2006 Performance tracking through the work compatibility model. Hum. Factors Ergon. Manuf. Serv. Ind. 16: 133–153

    Article  Google Scholar 

  20. Taber K S 2018 The use of Cronbach’s alpha when developing and reporting research instruments in science education. Res. Sci. Educ. 48: 1273–1296

    Article  Google Scholar 

  21. Daniel W W and Cross C L 2018 Biostatistics: a foundation for analysis in the health sciences. Wiley

    MATH  Google Scholar 

  22. Osborne J W 2008 Best practices in quantitative methods. SEGA Publications, Inc. Thousand Oaks, California, pp. 358–384

  23. Park H A 2013 An introduction to logistic regression: from basic concepts to interpretation with particular attention to nursing domain. J. Korean Acad. Nurs. 43: 154–164

    Article  Google Scholar 

  24. Charnes A, Cooper W W and Rhodes E 1978 Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2: 429–444

    Article  MathSciNet  MATH  Google Scholar 

  25. Adler N, Friedman L and Sinuany-Stern Z 2002 Review of ranking methods in the data envelopment analysis context. Eur. J. Oper. Res. 140: 249–265

    Article  MathSciNet  MATH  Google Scholar 

  26. Aldamak A and Zolfaghari S 2017 Review of efficiency ranking methods in data envelopment analysis. Meas. 106: 161–172

    Article  Google Scholar 

  27. Paradi J C and Zhu H 2013 A survey on bank branch efficiency and performance research with data envelopment analysis. Omega. 41: 61–79

    Article  Google Scholar 

  28. Yavas B F and Fisher D M 2005 Performance evaluation of commercial bank branches using data envelopment analysis. J. Busin. Manage. 11: 89–102

    Google Scholar 

  29. Paradi J C, Rouatt S and Zhu H 2011 Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega. 39: 99–109

    Article  Google Scholar 

  30. Smith G 2018 Step away from stepwise. J. Big Data. 5: 32

    Article  Google Scholar 

  31. Butani S J 1988 Relative risk analysis of injuries in coal mining by age and experience at present company. J. Occup. Accid. 10: 209–216

    Article  Google Scholar 

  32. Vernon H M and Bedford T. 1928 A Study of Absenteeism in a Group of Ten Collieries. Industrial Fatigue Research Board Report. Medical Research Council. 51: 68

  33. Arra K, Gunda Y R, Gupta S 2020 Work-Compatibility Based Accident Prediction Model for the Workforce of an Underground Coal Mine in India. Adv. Intell. Syst. Comput. pp. 544-550

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Authors and Affiliations

  1. Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, 221005, India

    Kumar Arra, Yuga Raju Gunda & Suprakash Gupta

Authors
  1. Kumar Arra
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  2. Yuga Raju Gunda
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  3. Suprakash Gupta
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Corresponding author

Correspondence to Suprakash Gupta.

Appendix: Questions for Questionnaire survey.

Appendix: Questions for Questionnaire survey.

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Arra, K., Gunda, Y.R. & Gupta, S. Development of a predictive model for workers' involvement in workplace accidents in an underground coal mine. Sādhanā 48, 63 (2023). https://doi.org/10.1007/s12046-023-02121-3

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  • DOI: https://doi.org/10.1007/s12046-023-02121-3

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