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http://dx.doi.org/10.1016/j.shaw.2017.01.001

Safety of Workers in Indian Mines: Study, Analysis, and Prediction  

Verma, Shikha (Yeshwantrao Chavan College of Engineering, Rashtrasant Tukadoji Maharaj Nagpur University)
Chaudhari, Sharad (Yeshwantrao Chavan College of Engineering, Rashtrasant Tukadoji Maharaj Nagpur University)
Publication Information
Safety and Health at Work / v.8, no.3, 2017 , pp. 267-275 More about this Journal
Abstract
Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.
Keywords
fuzzy reasoning approach; human factor analysis and classification system; mining safety; risk assessment;
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  • Reference
1 Government of India, Ministry of Labour & Employment. Directorate general of mines safety annual report; 2005. 25 p.
2 Government of India Ministry of Labour & Employment. Directorate general of mines safety annual report; 2009. 31 p.
3 Government of India Ministry of Labour & Employment. Directorate general of mines safety annual report; 2010. 28 p.
4 Government of India Ministry of Labour & Employment. Directorate general of mines safety annual report; 2012. 21 p.
5 Government of India Ministry of Labour & Employment. Directorate general of mines safety annual report; 2013. 34 p.
6 Government of India Ministry of Labour & Employment. Directorate general of mines safety annual report; 2014. 30 p.
7 Government of India Ministry of Labour & Employment. Directorate general of mines safety annual report; 2015. 32 p.
8 Society for Mining Research, Sustainable Development and Environment Kolkata. Final report on health and safety management system of an U/G mine 2010; Vols. 1-2.
9 Patterson MJ, Shappell AS. Operator error and system deficiencies: analysis of 508 mining incidents and accidents from Queensland, Australia using HFACS. Accident Anal Prev 2010;42:1379-85.   DOI
10 Wiegmann DA, Shappell SA. Human factor analysis of post-accident applying theoretical taxonomies of human error. Int J Aviat Psychol 1995;7:67-81.
11 Wiegmann DA, Rantanen EM. Defining the relationship between human error classes and technology intervention strategies. Aviation Human Factors Division Institute of Aviation; 2003. Report No.: AHFD-03-15/NASA-02-1. 45 p.
12 Dhillon BS, Raouf A. Safety assessment: a quantitative approach. Florida (US): Boca Raton; 1994. 200 p.
13 Wiegmann DA, Shappell SA. Human error perspectives in aviation. Int J Aviat Psychol 2000;11:341-57.
14 Wiegmann DA, Shappell SA. Applying the human factors analysis and classification system (HFACS) to the analysis of commercial aviation accident data. 11th International Symposium on Aviation Psychology. Columbus (OH): The Ohio State University (US); 2001. 16 p.
15 Wiegmann DA, Shappell SA. A human error analysis of commercial aviation accidents using the human factors analysis and classification system (HFACS). Aviation Medicine Washington (US); 2001. Report No.: D.C. 20591. 20 p.
16 Shappell AS, Weigmann AD. A human error analysis of general aviation controlled flight into terrain accidents occurring between 1990-1998: Office of Aerospace Medicine Washington (US); 2003. Report No.: D.C. 20591. 26 p.
17 Wiegmann AD, Detwiler C, Holcomb K, Hackworth C, Boquet A, Shappell S. Human error and general aviation accidents: a comprehensive, fine-grained analysis using HFACS. Office of Aerospace Medicine Washington (US); 2005. Report No.: DC 20591. 24 p.
18 Detwiler C, Hackworth C, Holcomb K, Boquet A, Pfleiderer E,Wiegmann D, Shappell S. Beneath the tip of the iceberg: a human factors analysis of general aviation accidents in Alaska versus the rest of the United States. Office of Aerospace Medicine; 2006. Report No.: DC20591. 16 p.
19 Shappell S, Detwiler C, Holcomb K, Hackworth C, Boquet A, Wiegmann AD. Human error and commercial aviation accidents: an analysis using the human factors analysis and classification system. Hum Factor 2007;49:227-42.   DOI
20 Shappell S, Wiegmann D. A methodology for assessing safety programs targeting human error in aviation. Int J Aviat Psychol 2009;19:252-69.   DOI
21 AT SB Transport Safety Report. Evaluation of the human factors analysis and classification system as a predictivemodel; 2011. Report No.: AR2008-036. 56 p.
22 Xi YT, Fang QG, Chen WJ, Hu SP. Case-based HFACS for collecting, classifying and analyzing human errors in marine accidents. Proc IEEE IEEM 2009:2148-53.
23 Wu J, Zhao T. C-HFAMF: a new way to accident analysis considering human factor. Proc IEEE 2011:319-22.
24 Wiegmann AD, Eggman AA, ElBardissi WA, Henrickson ES, Sundt MT. Improving cardiac surgical care: a work systems approach. Appl Ergon 2010;41:701-12.   DOI
25 Torfi F, Farahani R, Rezapour S. Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives. J Appl Soft Comput 2010;10:520-8.   DOI
26 Lenne GM, Salmon MP, Charles C, Trotter ML. A systems approach to accident causation in mining: an application of the HFACS method. Accident Anal Prev 2012;48:111-7.   DOI
27 Mamdani H, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. I J Man Mach Stud 1975;7:1-13.   DOI
28 Zadeh L. The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Set Syst 1983;11:199-227.   DOI
29 Collan M, Fedrizzi M, Luukka P. A multi-expert system for ranking patents: an approach based on fuzzy pay-off distributions and a TOPSIS-AHP framework. J Expert Syst Appl 2013;40:4749-59.   DOI
30 Aminbakhsh S, Gunduz M, Sonmez R. Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects. J Saf Res 2013;46:99-105.   DOI
31 Wang Y, Chain K, Poon G, Yang J. Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. J Expert Syst Appl 2009;36:1195-207.   DOI
32 Ung ST, Williams V, Bonsall S, Wang J. Test case based risk predictions using artificial neural network. J Saf Res 2006;37:245-60.   DOI
33 Ren J, Jenikson I, Wang J, Xu DL, Yang JB. A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors. J Saf Res 2008;39:87-100.   DOI
34 Baker JC, An M, Huang S. Railway risk assessment- the fuzzy reasoning approach and fuzzy analytic hierarchy process approaches: a case study of shunting at waterloo depot. J Rail Rapid Transit 2007;221:365-83.   DOI
35 Grassi A, Gamberini R, Mora C, Rimini B. A fuzzy multi-Attribute model for risk evaluation in workplaces. J Saf Sci 2009;47:707-16.   DOI
36 Gurcanli E, Mungen U. An occupational safety risk analysis method at construction site using fuzzy sets. Int J Ind Ergonom 2009;39:371-87.   DOI
37 Zhou J. SPA-fuzzy method based real-time risk assessment for major hazard installations storing flammable gas. J Saf Sci 2010;48:819-22.   DOI
38 Liu H, Tsai Y. A fuzzy risk assessment approach for occupational hazards in the construction industry. J Saf Sci 2012;50:1067-78.   DOI
39 Verma S, Chaudhri S. Highlights from the literature on risk assessment techniques adopted in the mining industry: a review of past contributions, recent developments and future scope. Int J Mining Sci Technol 2016;26:691-702.   DOI
40 Beriha G, Patnaik B, Padhee S. Assessment of safety performance in Indian industries using fuzzy approach. J Expert Syst Appl 2012;39:3311-23.   DOI
41 Verma S, Chaudhri S, Sandip K. A fuzzy risk assessment model applied for metalliferous mines In India. IEMCON2014 Conference on Electronics Engineering and Computer Science, Procedia Technology 2014. p. 202-13.
42 Zheng G, Zhu N, Tian Z, Chwn Y, Sun B. Application of trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid enviroments. J Saf Sci 2012;50:228-39.   DOI
43 Mahdevari S, Shahriar K, Esfahanipour A. Human health and safety risks management in underground coal mines using fuzzy TOPSIS. Sci Total Environ 2014;488-489:85-99.   DOI
44 Verma S, Chaudhri S. Integration of fuzzy reasoning approach (FRA) and fuzzy analytic hierarchy process (FAHP) for risk assessment in mining industry. J Ind Eng Manage 2014;7:1347-67.
45 Verma S, Chaudhri S. Fuzzy reasoning approach (FRA) for assessment of workers safety in manganese mines. Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 2016. p. 135-43.
46 Verma S, Gupta M. Risk assessment in mining industry. Int J Mining Miner Eng 2013;4:312-32.   DOI