Use of fuzzy set theory to rmr classification for weak and very weak rock masses

The Rock Mass Rating (RMR) system is an internationally recognized classification system that can be used for preliminary ground control design. The RMR system assigns quantifiable values to predefined classified parameters of a rock mass. Assigning a single value rather than a range to each parameter is a source of uncertainty, however, assigning a single value can be useful for further studies related to RMR ratings. Fuzzy systems are being used in an increasing number of application areas where linguistic rules are used to describe the system. In past decades, fuzzy systems have been successfully used in geotechnical and mining engineering problems to cope with uncertain data as well as with vagueness. In such scenarios, linguistic rules and rating-based classification of rock masses have always been questionable. The objective of this study is to apply fuzzy set theory to the RMR classification system. The fuzzy system proposed in this study is specified for weak and very weak rock masses (RMR<50) based on data collected from Nevada gold mines where 68% of RMR data is in the “poor” and “very poor” classes. In this approach, a Mamdani fuzzy algorithm has been constructed to evaluate basic RMR89 rating for the “wet condition.” Comparison between basic RMR89 and fuzzy RMR shows the fuzzy RMR system is valid to predict RMR from the discrete rating of each input parameter.