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Influence of data characterization process on the kinematic stability analysis of engineered rock slopes using discrete fracture network models and its implications for rock mass classification system

University of British Columbia

The thesis investigates the influence of data characterization process on kinematic slope stability analysis using a Discrete Fracture Network (DFN) approach. The first aspect of the data characterization process considered in this thesis is the influence of separate statistical procedure to define fracture set (aggregate vs disaggregate approach). The DFN models generated using aggregate and disaggregate approaches are compared in terms of simulated fracture properties and the kinematic slope stability analysis. The results showed the aggregate approach either overestimates or underestimates the important fracture properties such as fracture intensity and length. Accordingly, the number and volume of blocks formed on the slope would not be truly representative of field condition. The second aspect of data characterization process is the influence of conditioning (incorporation of mapped fractures) to DFN models. The unconditioned and conditioned DFN model are compared in terms of kinematic slope stability analysis, with emphasis on the locations of potential block formations. The results showed that the conditioned DFN model would allow for a better consideration of spatial locations of potentially unstable blocks. Lastly, the thesis presents the application of DFN approach to study the variability of Geological Strength Index (GSI). The Particle Size Distribution (PSD) plots obtained from DFN models are combined with the quantification method of GSI to estimate the GSI rating. Additionally, the implication of two-dimensional (2D) versus three-dimensional (3D) data to characterize rock mass blockiness is examined. The results showed that the range of GSI rating for a rock mass could be as large as ±10. This suggests the limitation on using a unique value of GSI rating, when the GSI rating is variable due to the inherent uncertainty of the rock mass in reality. The comparison between 2D and 3D blockiness showed that the blockiness observed on a 2D plane does not necessarily correspond to the true 3D blockiness of the rock mass. In these contexts, DFN models offer the opportunity to characterize this variability and provide better estimates of rock mass blockiness.

Text

2018-09-11

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/67145

Mining Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/