PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Improving the method of roof fall susceptibility assessment based on fuzzy approach

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Udoskonalenie metody określania skłonności stropu do zawału w oparciu o elementy logiki rozmytej
Języki publikacji
EN
Abstrakty
EN
Retreat mining is always accompanied by a great amount of accidents and most of them are due to roof fall. Therefore, development of methodologies to evaluate the roof fall susceptibility (RFS) seems essential. Ghasemi et al. (2012) proposed a systematic methodology to assess the roof fall risk during retreat mining based on risk assessment classic approach. The main defect of this method is ignorance of subjective uncertainties due to linguistic input value of some factors, low resolution, fixed weighting, sharp class boundaries, etc. To remove this defection and improve the mentioned method, in this paper, a novel methodology is presented to assess the RFS using fuzzy approach. The application of fuzzy approach provides an effective tool to handle the subjective uncertainties. Furthermore, fuzzy analytical hierarchy process (AHP) is used to structure and prioritize various risk factors and sub-factors during development of this method. This methodology is applied to identify the susceptibility of roof fall occurrence in main panel of Tabas Central Mine (TCM), Iran. The results indicate that this methodology is effective and efficient in assessing RFS.
PL
Wybieraniu w kierunku od pola towarzyszy zazwyczaj większa ilość wypadków, większość z nich spowodowana jest zawałem stropu. Dlatego też opracowanie skutecznej metody oceny skłonności stropu do zawału jest kwestią kluczową. Ghasemi et al. (2012) zaproponował metodologię określania ryzyka zawału stropu w trakcie prowadzenia prac górniczych w kierunku od pola w oparciu o klasyczne metody oceny ryzyka. Główną wadą tej metody jest to, iż nie uwzględnia ona subiektywnych niepewności na poziomie językowym związanych z określaniem wartości wejściowych charakteryzujących czynniki ryzyka, inne niedociągnięcia to niska rozdzielczość metody, stałe przyporządkowania wag, przyjęcie ostrych granic pomiędzy kolejnymi klasami. Aby usunąć te niedociągnięcia i w ten sposób udoskonalić metodę, zaproponowano nowe podejście do określania stabilności stropu wykorzystujące elementy logiki rozmytej. Zastosowanie logiki rozmytej jest efektywnym narzędziem w przypadku niepewności na poziomie językowym. Ponadto podejście bazujące na określeniu hierarchii procesów i wykorzystujące elementy logiki rozmytej zastosować można do określania wagi poszczególnych czynników ryzyka oraz czynników cząstkowych. Opracowaną metodę zastosowano do oceny skłonności stropu do zawału w polu głównym wybierania w kopalni Tabas Central Mine, w Iranie. Uzyskane wyniki potwierdzają skuteczność metody prognozowania stabilności stropu.
Rocznik
Strony
13--32
Opis fizyczny
Bibliogr. 46 poz., rys., tab.
Twórcy
autor
  • Department of Mining Engineering, Isfahan University of Technology, Isfahan, P.O. Box 8415683111, Iran
autor
  • Department of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, P.O. Box 3619995161, Iran
autor
  • Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, P.O. Box 158754413, Iran
Bibliografia
  • [1] Buckley J.J., 1985. Fuzzy hierarchical analysis. Fuzzy Sets and Systems, Vol. 17, p. 233-247.
  • [2] Chang D.Y., 1996. Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, Vol. 95, p. 649-655.
  • [3] Chase F.E., McComas A., Mark C., Goble C.D., 1997. Retreat mining with mobile roof supports. Proceedings of the new technology for ground control in retreat mining. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, NIOSH Publication No. 9446, p. 74-88.
  • [4] Cheng C.H., 1997. Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research, Vol. 96, p. 343-350.
  • [5] Dagdeviren M., Yuksel I., 2008. Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management. Information Sciences, Vol. 178, p. 1717-1733.
  • [6] Deb D., 2003. Analysis of coal mine roof fall rate using fuzzy reasoning techniques. International Journal of Rock Mechanics and Mining Sciences, Vol. 40, p. 251-257.
  • [7] Deng H., 1999. Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate Reasoning, Vol. 21, p. 215-231.
  • [8] Duzgun H.S.B., 2005. Analysis of roof fall hazards and risk assessment for Zanguldak coal basin underground mines. International Journal of Coal Geology, Vol. 64, p. 104-115.
  • [9] Duzgun H.S.B., Einstein H.H., 2004. Assessment and management of roof fall risks in underground coal mines. Safety Science, Vol. 42, p. 23-41.
  • [10] Erensal Y.C., Ozcan T., Demircan M.L., 2006. Determining key capabilities in technology management using fuzzy analytic hierarchy process: a case study of Turkey. Information Sciences, Vol. 176, p. 2755-2770.
  • [11] Farid M., Hossein Abadi M.M., Yazdani-Chamzini A., Yakhchali S.H., Basiri M.H., 2013. Developing a new model based on neuro-fuzzy system for predicting roof fall in coal mines. Neural Computing and Applications, Vol. 23, p. 129-137.
  • [12] Feddock J.E., Ma J., 2006. Safety: a review and evaluation of current retreat mining practice in Kentucky. Proceedings of the 25th international conference on ground control in mining, Morgantown, West Virginia University, USA, p. 366-373.
  • [13] Ghasemi E., Ataei M., 2013. Application of fuzzy logic for predicting roof fall rate in coal mines. Neural Computing and Applications, Vol. 22, p. 311-321.
  • [14] Ghasemi E., Ataei M., Shahriar K., Sereshki F., Jalali S.E., Ramazanzadeh A., 2012. Assessment of roof fall risk during retreat mining in room and pillar coal mines. International Journal of Rock Mechnics and Mining Sciences, Vol. 54, p. 80-89.
  • [15] Ghasemi E., Shahriar K., Sharifzadeh M., Hashemolhosseini H., 2010. Quantifying the uncertainty of pillar safety factor by Monte Carlo simulation- a case study. Archives of Mining Sciences, Vol. 55, p. 623-635.
  • [16] Kahraman C., Ertay T., Buyukozkan G., 2006. A fuzzy optimization model for QFD planning process using analytic network approach. European Journal of Operational Research, Vol. 171, p. 390-411.
  • [17] Klemetti T., Molinda G.M., 2009. Comparative analysis of moisture sensitivity index test for coal mine roof. SME annual meeting and exhibit. Society for Mining, Metallurgy and Exploration Inc, preprint 09-068, p. 1-5.
  • [18] Lee H.M., 1996. Applying fuzzy set theory to evaluate the rate of aggregative risk in software development. Fuzzy Sets and Systems, Vol. 79, p. 323-336.
  • [19] Leung L.C., Cao D., 2000. On consistency and ranking of alternatives in fuzzy AHP. European Journal of Operational Research, Vol. 124, p. 102-113.
  • [20] Lind G.H., 2005. Risk management coal pillar extraction in South Africa. International Journal of Surface Mining Reclamation and Environment, Vol. 19, p. 218-233.
  • [21] Maiti J., Khanzode V.V., 2009. Development of a relative risk model for roof and side fall fatal accidents in underground coal mines in India. Safety Science, Vol. 47, p. 1068-1076.
  • [22] Maleki H., 2008. Towards the development of integrated monitoring system for retreat mining operations. Journal of Coal Science and Engineering, Vol. 14, p. 477-484.
  • [23] Mark C., Chase F.E., Pappas D.M., 2003. Reducing the risk of ground falls during pillar recovery. SME Transactions, Vol. 314, p. 153-160.
  • [24] Mark C., Karabin G., Zelanko J.C., Hoch M.T., Chase F.E., 2002. Evaluation of pillar recovery in southern West Virginia. Proceedings of the 21st international conference on ground control in mining, Morgantown, West Virginia University, USA, p. 81-89.
  • [25] Mark C., Pappas D.M., Barczak T.M., 2009. Current trends in reducing ground fall accidents in US coal mines. Mining Engineering, Vol. 63, p. 60-65.
  • [26] Mark C., Zelanko J.C., 2001. Sizing of final stumps for safer pillar extraction. Proceedings of the 20th international conference on ground control in mining, Morgantown, West Virginia University, USA, p. 59-66.
  • [27] Mark C., Zelanko J.C., 2005. Reducing roof fall accidents on retreat mining sections. Coal Age, Vol. 110, p. 26-31.
  • [28] Markowski A.S., Mannan M.S., 2009. Fuzzy logic for piping risk assessment (pfLOPA). Journal of Loss Prevention in the Process Industries, Vol. 22, p. 921-927.
  • [29] Mikaeil R., Naghadehi M.Z., Ataei M., Khalokakaie R., 2009. A decision support system using fuzzy analytical hierarchy process (FAHP) and TOPSIS approaches for selection of the optimum underground mining method. Arch. Min. Sci. 54, 349-368.
  • [30] Mikhailov L., 2004. A fuzzy approach to deriving priorities from interval pairwise comparison judgments. European Journal of Operational Research, Vol. 159, p. 687-704.
  • [31] Miri Lavasani S.M., Yang Z., Finlay J., Wang J., 2011. Fuzzy risk assessment of oil and gas offshore wells. Process Safety and Environmental Protection, Vol. 89, p. 277-294.
  • [32] Molinda G.M., 2003. Geologic hazards and roof stability in coal mines. US Department of Health and Human Services, Center for Disease Control and Prevention, National Institute for Occupational Safety and Health, NIOSH Publication No. 9466.
  • [33] Molinda G.M., Mark C., Dolinar D., 2000. Assessing coal mine roof stability through roof fall analysis. In: Proceedings of the new technology for coal mine roof support. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, NIOSH Publication No. 9453, p. 53-72.
  • [34] Nieto-Morrote A., Ruz-Vila F., 2011. A fuzzy approach to construction project risk assessment. International Journal of Project Management, Vol. 29, p. 220-231.
  • [35] Palei S.K., Das S.K., 2008. Sensitivity analysis of support safety factor for predicting the effects of contributing parameters on roof falls in underground coal mines. International Journal of Coal Geology, Vol. 75, p. 241-247.
  • [36] Palei S.K., Das S.K., 2009. Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: an approach. Safety Science, Vol. 47, p. 88-96.
  • [37] Peng S.S., 2008. Coal mine ground control. Published by Syd S. Peng, Department of Mining Engineering, College of Engineering and Mineral Resources, Morgantown, West Virginia University, USA.
  • [38] Razani M., Yazdani-Chamzini A., Yakhchali S.H., 2013. A novel fuzzy inference system for predicting roof fall rate in underground coal mines. Safety Science, Vol. 55, p. 26-33.
  • [39] Saaty T.L., 1980. The analytic hierarchy process. Mcgraw-Hill, New York.
  • [40] Saboya Jr F., Alves M.G., Pinto W.D., 2006. Assessment of failure susceptibility of soil slops using fuzzy logic. Engineering Geology, Vol. 86, p. 211-224.
  • [41] Sadiq R., Husain T., 2005. A fuzzy-based methodology for an aggregative environmental risk assessment: a case study of drilling waste. Environmental Modelling and Software, Vol. 20, p. 33-46.
  • [42] Shahriar K., Bakhtavar E., 2009. Geotechnical risks in underground coal mines. Journal of Applied Sciences, Vol. 9, p. 2137-2143.
  • [43] van der Merwe J.N., van Vuuren J.J., Butcher R., Canbulat I., 2001. Causes of falls of roof in South African collieries. Safety in Mines Research Advisory Committee (SIMRAC). Final Project Report, Report No. COL613.
  • [44] Van Laarhoven P.J.M., Pedrycz W., 1983. A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, Vol. 11, p. 229-241.
  • [45] Wang Y., Elhag T., 2007. A fuzzy group decision making approach for bridge risk assessment. Computers and Industrial Engineering, Vol. 53, p. 137-148.
  • [46] Zadeh L.A., 1965. Fuzzy sets. Information and Control, Vol. 8, p. 338-353.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d47b1a50-c899-48c8-9f60-2152184e022e
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.