Probabilistic rock mass rating estimation using electrical resistivity

Abstract

Understanding exact rock conditions has a significant impact on the success of tunnel construction. Various methods such as Q-systems and rock mass rating (RMR) have been used to estimate the condition of jointed rock mass in fields. However, the evaluation of rock mass characteristics can diverge because of the subjective views of observers. Many researchers that work on the relationship between rock mass classification methods and its physical variables have tried to solve this problem; nevertheless, the accuracy of their studies has become a subject of controversy. Thus, in this study, we tried to evaluate rock mass classification in terms ofRMRfrom the electrical resistivity that is readily affected by the characteristics of jointed rock mass. The relationship between theRMRparameters and the electrical resistivity was deducted using the formula of electrical resistivity analyzed for jointed rock mass. The correlation analyses were performed based on real data (4,689 sets) in order to obtain the degree of correlation amongRMRparameters. The results show that some parameters are strongly correlated while other parameters are not. Stochastic analyses show that the theoretical relationship betweenRMRvalue and electrical resistivity can be improved by probabilistically removing the irrelevant (low possibility) relation amongRMRparameters. Finally, a field test was performed to verify the suggested probabilisticRMRestimation using electrical resistivity.