Rock Mass Classification using Fuzzy Sets

It is widely recognized that subjectivity plays an important role in mining geomechanics in general and is the basis of a number of criteria employed in many different rock mass classificationsystema.

Subjective uncertainty results mainly from the use of descriptive and sometimes ambiguous phrases in the processing of geotechnical information required in rock mass classification, e.g. “highly weathered rock”, “very large inflow of water”. For many other classes of criteria that can be treated as quantitative, such as unconfined compressive strength, slaking potential, etc., it can be recognized that the numerical values of these criteria already possess some inherent uncertainty associated with test or observation errors customarily dealt with by statistics and probability theory. Furthermore, some index values for these criteria may lie in the boundary or cloudy region between two adjacent classes, and yet by the conventional scores summation method, we normally assign a fixed numerical score to an item within a corresponding range regardless of the relative position of the item value in the range.

In this paper, we present the application of fuzzy set theory, and in particular, the Bellman-Zadeh aggregation procedure in obtaining a rock mass classification rating from the CSIR classification system with incorporation of expert knowledge. Numerical examples are given to illustrate the proposed procedure.