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UBC Theses and Dissertations

A sensitivity analysis for the network connectivity index (NCI) using discrete fracture networks (DFN) Fogel, Yaniv

Abstract

The characterization and classification of rock mass structures is a key step in any rock engineering operation. The commonly used methods today, such as Rock Quality Designation (RQD), Rock Mass Rating (RMR), Tunneling Quality Index (Q-System), and Geological Strength Index (GSI) are based on empirical correlations and the subjective judgment of the user. Geotechnical projects are becoming larger and much more complex than when these methods were first developed, some 40 years ago. The development of new methods should focus on taking advantage of recent advances in mapping, remote sensing, and automation to reduce bias and errors. (Elmo, Yang, et al., 2021) proposed the Network Connectivity Index (NCI) as an alternative rock mass quality indicator. With the potential of being utilized fully using only remote sensing methods, this approach is further evaluated. Using Discrete Fracture Network (DFN) models, a sensitivity analysis was performed to assess the impact of structural properties and window sampling procedures on the variability of NCI results. The DFN methodology was reviewed, and input parameters were defined for generating a set of base case models. An additional set of models, based on DFN input parameters from existing projects was also generated. 2D sections were extracted from each model as analogs for rock exposures. A Python algorithm incorporated grid-based and random-based window sampling of the 2D sections, and an NCI value was calculated for each window. The impact of input rock mass structural properties on the variability of NCI values was evaluated, focusing on fracture size distributions, mean fracture size, and trace length data. The impact of the sampling procedures was also investigated, focusing on the sampling window pattern, size, shape, and rotation. The potential to correlate between NCI results and volumetric fracture intensity (P₃₂) values were also checked. The analysis showed that NCI is scale-dependent. As sampling windows increase in size, NCI values increase. The variability of NCI results is sensitive to the persistence of features in the rock exposure, with longer features having a larger impact and resulting in higher NCI values. Finally, a correlation between NCI and P₃₂ could not be established.

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Attribution-NonCommercial-NoDerivatives 4.0 International