Influence of persistence on behaviour of fractured rock masses
Bibliographic record
Abstract
Abstract Most of the models that simulate fractured rock masses assume fully persistent discontinuities, simplifying the fact that, in nature, fractured rock masses are made of non-continuous sets of joints. A rock bridge gives an effective cohesion to the fracture and a block of rock cannot fall or slide until all the rock bridges fail. This failure involves the failure of the intact rock, which can be orders of magnitude stronger than the shear strength of the rock joint. In this study we focus on how the distribution of rock bridges influences the overall rock mass behaviour, to contribute to the understanding of how the presence of rock bridges influences the ‘scale’ effect that is observed between strength values measured on intact rock in the laboratory and those observed at the rock mass scale. To estimate the influence of spatial fracture, parameters of the rock mass strength and deformation were determined, using the orthogonal arrays method, UDEC and variance analysis. The numerical model was first calibrated on shear tests of samples made of continuous joints, and then used to investigate the shear behaviour of a fractured rock mass with non-persistent joints. The 2D approach was successfully extended to 3D models using 3DEC with the aim of providing a better approach for simulating the stability of an underground cavern in a fractured rockmass.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".