204 Rock mass classification
LO[ A RQD , LO[ /y , LOI ~~ I.O[
0.0 0.0 0.0 0.0 ,
40 50 60 70 80 90 0 I 2 3 4 0 051.015 8 9 10 11 12 130.0 *.ONRF 123
Figure 12.4 Application of fuzzy methods to the assessment of Q.classification of 8.0 and 1.1, respectively, are found from the corresponding
values of the individual parameters. The distribution of the remaining
values of the number are found by combining the values of the individual
parameters at membership values of 0.1,0.2 and so on. It is interesting to
see that the result is a number in which the distribution of values is non-
linear: the 'flanks' of the number are curved.
The conclusions to be drawn from this visual examination of the result are
that there is more possibility of Q being less than the most likely value rather
than being greater, and that the convex nature of the flanks has the effect of
increasing the possibility that the conditions will be worse than a single-
valued calculation would imply. Lastly, it should be noted that the final
distribution of Q and the associated conclusions are not at all obvious from
an examination of the nature of the original fuzzy component parameters.
12.6.2 Use of RES (Rock Engineering Systems)
The principle behind the RES system (Hudson, 1992) is that the informa-
tion obtained should match the engineering objective. The two main
classification systems-those of RMR and Q-utilize six main parameters
which are not the same. The developers of these systems have decided on
which parameters are most important for tunnel design, and designed their
classifications accordingly. Both proponents of the systems have warned
users not to attempt to extrapolate the classification methods without
modification and not to make predictions outside the original subjects for
which the classification schemes were intended.
A more general approach is to consider for any specific project the
relative importance of all rock engineering parameters, and then to
concentrate on the most important, say, six or 10 parameters. One could
go further and establish how many parameters contributed to, say, 95% of
the design process, and allocate resources accordingly. To illustrate this
point, we refer the reader to Fig. 11.2 in which the parameters associated