Influence of Fracture Characteristics and the Use of Network Connectivity Index on Synthetic Rock Mass Strength Characterisation of Moderately Jointed Granite in Hong Kong
DOI:
https://doi.org/10.21467/proceedings.7.7.1Abstract
Studies by Elmo & Stead (2010) and Stavrou et al. (2019) demonstrated that rock mass strength can be quantitatively characterised and directly linked to fracture characteristics. However, to date, rock mass classification and strength characterisation processes remain largely inductive, relying on empirical methods. It has been identified that this process is influenced by subjectivity and engineering judgement. With the rise of computing power and numerical modelling tools, engineers tend to apply and extend the use of rock mass classification system to determine rock mass strength and parameters in more advanced analyses. Typically, assessment of rock mass strength is undertaken by correlating different rock mass classification systems with the Hoek- Brown criterion for engineering and design purposes. Fracture characteristics; in terms of intensity and trace lengths, are rarely analysed in detail. This study addresses this gap by evaluating the influence of fracture characteristics on rock mass strength. The focus is on the assessment of unconfined compressive strength (UCS) of moderately jointed granite, based on actual tunnel face mapping records. Virtual large scale UCS tests on Synthetic Rock Masses (SRM), which consist of Voronoi Grain-Based Models (GBM) and explicit fractures, are undertaken to determine the rock mass strengths. The results indicate that fracture characteristics poses significant influence on the SRM strengths and demonstrate that rock mass strength characterisation can be directly assessed through the Network Connectivity Index (NCI, Elmo et al. (2021), which considers explicit assessment of fracture intensity & trace lengths. Through clear correlations between rock mass strength and NCI values, this paper demonstrates that NCI provides a more robust and objective alternative to the commonly used Q'-GSI correlations in deriving rock mass strength. The findings demonstrate the importance of integrating detailed fracture assessment into rock mass classification and mapping processes, to improve reliability of rock mass strength and parameters prediction for engineering purposes.
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