Assessing rock mass UCS anisotropy using a coupled DFN-DEM approach at a surface mining project in Artic Canada
Bibliographic record
Abstract
A coupled DFN-DEM approach was used to evaluate the anisotropy of rock mass mechanical compressive strength (UCS) at Representative Elemental Volume (REV) at the feasibility stage of an open pit mining project located in Nunavut Territory in northern Canada. The paper presents modelling work performed to define the rock mass UCS based on field data and on laboratory testing results. In particular, the influence of the in-situ rock mass structural properties variations on the rock mass UCS is studied. To begin with, a discussion is presented on the difficulty to account for the anisotropy of the rock mass UCS at engineering scale. Coupled DFN-DEM modelling was shown to be useful although still rarely used mainly due to computing time limitation. In this paper, the modelling workflow is outlined. DFN modelling results are presented - several DFNs were modelled to fully represent the spectrum of possible structural regimes at the site. The DFN-DEM calibration process for simulating rock mass samples at REV size is also presented. DFN-DEM models were created for all generated DFNs – representing the observed spectrum of the structural rock mass properties. The rock mass UCS was evaluated for all samples and the anisotropy of the rock mass UCS was then calculated. The results suggest that for this mining site, the rock mass at REV size is strongly anisotropic. Furthermore, they suggest that the rock mass structural variability significantly affects the rock mass anisotropy. The results not only highlight the possibilities associated with DFNDEM modelling in characterizing rock mass anisotropy at the engineering scale, they also provide a systematic way to assess the variability in rock mass properties anisotropy for engineering works.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".