Since, in geomechanical evaluation of rock masses, many parameters affect the rock mass behavior simultaneously, rock engineering classifications are suitable approaches for studying and prediction of rock mass behaviors. Classic classifications in rock engineering have suffer from some practical limitations, particularly . These limitations are more obvious in when classification is made near the boundaries. y conditions. In this study in order to increase the abilities and expending the applications of rock mass drillability index (RDi), fuzzy theory has been used. For this purpose, six parameters of the rock mass which are used in RDi classification, including uniaxial compressive strength (UCS), joints dipping, Mohs hardness, joints aperture, joints spacing and grain size have been used. Then, some fuzzy functions have been defined on these parameters and finally the class of each rock mass has been identified. In order to compare the results of classic classification with results of fuzzy classification, a case study was done on rock masses of limestone mine of Shahrood cement factory. The results show that the fuzzy logic based classification produces clearer better results than classic system especially in rock masses with boundary condition.
During this research, a software was prepared for doing theto calculate the RDi scoresions and classifying the rock mass. Theis program has been written by coded in Visual C++ and has a contains a graphical main window thatinterface with all input parameters which are related (rock mass characteristics) and also the out output parameters of the program ( which are RDi score and class of rock mass). are illustrated in it.
Khalokakaei, R., Ataei, M., Hoseini, S., & Mikaeil, R. (2012). Development of a Fuzzy Classification for Prediction of Penetration Rate of Drilling in Mines. Journal of Mining Engineering, 7(16), 89-99.
MLA
R Khalokakaei; M Ataei; S.H Hoseini; R Mikaeil. "Development of a Fuzzy Classification for Prediction of Penetration Rate of Drilling in Mines". Journal of Mining Engineering, 7, 16, 2012, 89-99.
HARVARD
Khalokakaei, R., Ataei, M., Hoseini, S., Mikaeil, R. (2012). 'Development of a Fuzzy Classification for Prediction of Penetration Rate of Drilling in Mines', Journal of Mining Engineering, 7(16), pp. 89-99.
VANCOUVER
Khalokakaei, R., Ataei, M., Hoseini, S., Mikaeil, R. Development of a Fuzzy Classification for Prediction of Penetration Rate of Drilling in Mines. Journal of Mining Engineering, 2012; 7(16): 89-99.
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