Content area
Full Text
ABSTRACT
The First step in analysis of slope stability in open pit mines is punctual definition of rock mass characteristics. Several rock mass classification systems have been presented so far in the area of geomechanics. One of the most widely used rock mass classification systems are the geomechanics classification (RMR) by Bieniawiski. The RMR classification is based on the definition of classic membership functions. So characterization of rock masses and determination of their strength may involve some uncertainties due to their complex nature. The fuzzy set theory is one of the tools to handle such uncertainties. This paper describes the application of fuzzy set theory to the RMR system by incorporating fuzzy sets, and mamadani fuzzy algorithm was constructed using "if-then" rules for evaluating RMR parameters and their rating considered in the RMR system. Firstly, RMR classification is redefined by using the fuzzy logic. In the second step, the tables for a case study in Iran are calculated based on field and laboratory measurements.
Keywords: Slope stability, Open pit mine, Rock mass rating (RMR), Classification, Fuzzy logic.
1. INTRODUCTION
In 1973 Bieniawski introduced 'Rock Mass Rating' (RMR), a new system of rock mass classification, also known as CSIR classification which included eight rock parameters, one of those paprameters is 'strike and dip orientations of joints'. Emphasis was given to the use of RMR classification in tunnels. In the second version of RMR classification some major changes were introduced (Bieniawski, 1976). Five rock mass parameters were added to obtain the numerical RMR value. From this RMR value, a 'rating adjustment for discontinuity orientations' (always a negative number) was subtracted. Some minor modifications were made in, and the actual form of RMR rating was established (Bieniawski, 1979).
The RMR classification is based on the definition of classic membership functions. So characterization of rock masses and determination of their strength may involve some uncertainties due to their complex nature.
The fuzzy set theory is one of the tools to handle such uncertainties. For these type systems, the fuzzy sets introduced by Zadeh (1965) may provide an effective solution. As illustrated in Fig. 1, contrary to crisp sets, a fuzzy set is composed of same objects and their corresponding degrees of membership in the set Crisp sets of...