Capturing the complete stress–strain behaviour of jointed rock using a numerical approach
Bibliographic record
Abstract
Summary This paper presents the results of a series of numerical experiments using the synthetic rock mass (SRM) approach to quantify the behaviour of jointed rock masses. Field data from a massive sulphide rock mass, at the Brunswick mine, were used to develop a discrete fracture network (DFN). The constructed DFN model was subsequently subjected to random sampling whereby 40 cubic samples, of height to width ratio of two, and of varying widths (0.05 to 10 m) were isolated. The discrete fracture samples were linked to 3D bonded particle models to generate representative SRM models for each sample size. This approach simulated the jointed rock mass as an assembly of fractures embedded into the rock matrix. The SRM samples were submitted to uniaxial loading, and the complete stress–strain behaviour of each specimen was recorded. This approach provided a way to determine the complex constitutive behaviour of large‐scale rock mass samples. This is often difficult or not possible to achieve in the laboratory. The numerical experiments suggested that higher post‐peak modulus values were obtained for smaller samples and lower values for larger sample sizes. Furthermore, the observed deviation of the recorded post‐peak modulus values decreased with sample size. The ratio of residual strength of rock mass samples per uniaxial compressive strength intact increases moderately with sample size. Consequently, for the investigated massive sulphide rock mass, the pre‐peak and post‐peak representative elemental volume size was found to be the same (7 × 7 × 14 m). Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".