Abstract

The Hoek–Brown constant m_i is a key input parameter in the Hoek–Brown failure criterion developed for estimating rock mass properties. The Hoek–Brown constant m_i values are traditionally estimated from results of triaxial compression tests, but these tests are time-consuming and expensive. In the absence of laboratory test data, guideline chart and empirical regression models have been proposed in the literature to estimate m_i values, and they give a general trend of m_i. Instead of only using either the guideline chart or regression models, information from both sources can be systematically integrated to improve estimates of m_i. In this study, a Bayesian approach is developed for probabilistic characterization of m_i, using information from guideline chart, regression model and site-specific uniaxial compression strength (UCS) test values. The probabilistic characterization of m_i provides a large number of m_i samples for conventional statistical analysis of m_i, including its full probability distribution. The proposed approach is illustrated and validated using real UCS and triaxial compression test data from a granite site at Forsmark, Sweden. To evaluate the reliability of the proposed method, m_i values estimated from the proposed method are compared with those predicted from a separate analysis which uses triaxial compression tests data. In addition, a sensitivity study is performed to explore the effect of site-specific input on the evolution of m_i. The approach provides reasonable statistics and probability distribution of m_i at a specific site, and the m_i samples can be directly used in rock engineering design and analysis, especially in Hoek–Brown failure criterion to predict rock failure.