نوع مقاله : علمی - پژوهشی
1 دانشآموخته کارشناسی ارشد مهندسی معدن، دانشگاه تربیت مدرس، تهران
2 استادیار گروه مهندسی معدن، دانشگاه صنعتی ارومیه
3 استادیار گروه مهندسی معدن، دانشکده فنی و مهندسی، دانشگاه ارومیه، ارومیه
عنوان مقاله [English]
Mining industry has an essential role in the economic and industrial development of a country by providing minerals. However, the mining industry faces considerable challenges in the field of environmental health and safety of its staff. The mining industry is a high-risk activity and a large number of miners are injured/killed each year. The problems related to miners' safety due to the exceptional condition in underground mines are more complicated. To reduce hazards and risks in the case of underground mines, identifying the most important factors that affect hazards is significant. Thus, controlling the accidents is possible as well as safety will increase by distinguishing the effective factors in the underground mining risks. In this study, 21 factors that have significant roles in underground mines accidents are identified. The factors classified into three categories, i.e., direct factors, work environment factors and systematic factors. These factors were evaluated using the fuzzy cognitive mapping (FCM) method and the casual-effect relationships between them specified. Notably, theory of Z-numbers used to overcome the uncertainty corresponding to causal relationship between the factors. Finally, the relationships between concepts were analyzed utilizing a hybrid learning algorithm and the final weight of each factor was obtained during 30 iterations. The obtained weights were prioritized and the results showed that the "falling" and "insensibility to hazards" factors with weights of 0.999 and 0.641, respectively, have the highest and lowest impact on the occurrence of underground mines accidents. It should be noticed that the factor of the fall, simultaneously with other factors, affects the occurrence of accidents in underground mines.
منابع 1. J. P. Leigh, G. Waehrer, T. R. Miller, and C. Keenan, “Costs of occupational injury and illness across industries,” Scandinavian journal of work, environment & health, pp. 199–205, 2004. 2. J. M. Patterson and S. A. Shappell, “Operator error and system deficiencies: analysis of 508 mining incidents and accidents from Queensland, Australia using HFACS,” Accident Analysis & Prevention., vol. 42, no. 4, pp. 1379–1385, 2010. 3. J. Bonsu, W. Van Dyk, J. P. Franzidis, F. Petersen, and A. Isafiade, “A systemic study of mining accident causality: an analysis of 91 mining accidents from a platinum mine in South Africa,” Journal of the Southern African Institute of Mining and Metallurgy., vol. 117, no. 1, pp. 59–66, 2017. 4. پورمیرزایی، ر.؛ 1396؛ "بررسی نقش صنعت معدنکاری و منابع معدنی در توسعه پایدار کشور". نشریه مهندسی منابع معدنی، دوره دوم، شماره 3 ،ص 12-1. 5. سایت مرکز آمار ایران: https://www.amar.org.ir 6. C. Nussey, “Studies of accidents leading to minor injuries in the UK coal mining industry∗,” Journal of Occupational Accidents., vol. 2, no. 4, pp. 305–323, 1980. 7. L. Wang, Y.-P. Cheng, and H.-Y. Liu, “An analysis of fatal gas accidents in Chinese coal mines,” Safety Science., vol. 62, pp. 107–113, 2014. 8. Y. Zhang, W. Shao, M. Zhang, H. Li, S. Yin, and Y. Xu, “Analysis 320 coal mine 1 using structural equation modeling with unsafe conditions of the rules and regulations as exogenous variables,” Accident Analysis & Prevention., vol. 92, pp. 189–201, 2016. 9. J. D. Bennett and D. L. Passmore, “Probability of death, disability, and restricted work activity in United States underground bituminous coal mines, 1975-1981,” Journal of Safety Research., 1984. 10. A. Asfaw, C. Mark, and R. Pana-Cryan, “Profitability and occupational injuries in US underground coal mines,” Accid. Anal. Prev., vol. 50, pp. 778–786, 2013. 11. H. S. B. Duzgun and H. H. Einstein, “Assessment and management of roof fall risks in underground coal mines,” Safety Science., vol. 42, no. 1, pp. 23–41, 2004. 12. B. Kosko, “Fuzzy cognitive maps,” International journal of man-machine studies., vol. 24, no. 1, pp. 65–75, 1986. 13. E. I. Papageorgiou, C. Stylios, and P. P. Groumpos, “Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links,” International Journal of Human-Computer Studies., vol. 64, no. 8, pp. 727–743, 2006. 14. E. I. Papageorgiou and A. Kannappan, “Fuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification,” Applied Soft Computing., vol. 12, no. 12, pp. 3798–3809, 2012. 15. E. Bakhtavar, S. Hosseini, K. Hewage, and R. Sadiq, “Green blasting policy: Simultaneous forecast of vertical and horizontal distribution of dust emissions using artificial causality-weighted neural network,” Journal of Cleaner Production., vol. 283, p. 124562, 2021. 16. C. C. Chou, “The canonical representation of multiplication operation on triangular fuzzy numbers,” Comput. Math. with Appl., 2003. 17. Nguyen, H. T., Md Dawal, S. Z., Nukman, Y., Aoyama, H., & Case, K. “An integrated approach of fuzzy linguistic preference based AHP and fuzzy COPRAS for machine tool evaluation“. PloS one, 10(9), e0133599. 2015. 18. L. A. Zadeh, “A note on Z-numbers,” Information Sciences. (Ny)., vol. 181, no. 14, pp. 2923–2932, 2011. 19. Jumarni, R. F., & Zamri, N. "A new concept of fuzzy TOPSIS and fuzzy logic in a multi-criteria decision." International Conference on Soft Computing and Data Mining, pp. 161-170. Springer, Cham, 2018. 20. بخت آور، ع.، شاهمرادی، م.، رحمتی، س.؛"1397؛ " ارزیابی تاثیر عوامل مشکلساز در ایجاد مخاطرات شغلی معادن زیرزمینی زغالسنگ ایران با رویکرد فازی اثرات متقابل علت و معلولی. نشریه مهندسی معدن, 13(40), 34-45. 21. Hull, B. P., Leigh, J., Driscoll, T. R., & Mandryk, J. "Factors associated with occupational injury severity in the New South Wales underground coal mining industry. " Safety Science, 21(3), 191-204. 1996.