Geomechanics classification (Rock Mass Rating - RMR) is the most widely used rock mass classification given by Z.T.Bieniawski between 1972 and 1973. It is based upon six parameters out of which five are universal and the sixth one is used specifically for different applications. Prediction of
RMR by the use of fuzzy logic makes it easier to predict the rating of rock more or less the same as calculated from experimental data. It becomes of great importance at the moment when we don’t have the rating tables of the six parameters, then by the use of fuzzy membership functions, we can approximately predict the RMR of the rock.
Introduction
While working on fields, it is impossible to predict the RMR by just looking at the experimental data provided, since that involves a lot of beforehand experiment of the rock samples. So by the use of membership functions, we can easily get an idea about the quality of rock which will give more or less the same value as we would have got from the experimental data.
Bieniawski’s geomechanics classification system provides a general rock mass rating (RMR) increasing with rock quality from 0 to 100. The five universal parameters are: strength of the rock, drill core quality, groundwater conditions, joint and fracture spacing and joint characteristics. The sixth parameter, orientation of joints, is entered differently for specific application in tunnelling, mining and foundations.
Increments of rock mass rating corresponding to each parameter are summed to determine RMR.
Tables 1 to 5 show the experimental data of universal parameters.
Table 6: Geomechanics Classification of Rock
Masses
Table 1: Rock Mass Ratin...
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... (∑ Wi * Xi) / (∑Wi) (1)
With the above given formula, we can easily calculate the degree of membership of RMR.
So, XRMR = (0.8 * 3 + 0.9 * 4 + 0.1 * 2 + 0.7 * 5 + 0.5 * 1) / (3+4+2+5+1) = 0.68
Corresponding to 0.68 degree of membership in the figure 6 of RMR, we get approximately, the value of RMR to be in-between 65-70, which represents a good quality of rock.
Result
The fuzzy logic gives approximately the same result as it would be obtained from the actual experiment or from the experimental results.
Conclusion
Fuzzy logic is designed to solve problems in the same way that humans do: by considering all available information and making the best possible decision given the input.
Bieniawski’s rock mass classification incorporates geological, geometric and design/engineering parameters in arriving at a quantitative value of its rock mass quality.
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