A new discrete fracture modelling approach for rock masses
Bibliographic record
Abstract
A method for modelling discrete fracture in rock masses under tensile and compressive stress fields is presented, based on a Mohr–Coulomb failure surface in compression and three independent anisotropic rotating crack models in tension. Extension fracturing is modelled by coupling the softening of the anisotropic rotating crack failure criterion to the compressive plastic strain evolution. An explicitly time-integrated coupled discrete element/finite element approach is employed with an explicit Lagrangian contact algorithm to enforce non-penetration of the surfaces created when the tensile strength is depleted. The geomechanical model is applied to naturally fractured rock masses, which are characterised by field mapping and borehole data integrated in a stochastic 3D discrete fracture network model. This approach maximises the utility of the field data and provides a direct approach to the determination of rock mass strength and deformability. It also provides a realistic insight into complex failure mechanisms. Typical applications are mine pillars, roofs, hangingwalls and block caving. The model has also been used on rock slopes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".