Strength characterization of rock masses, using a coupled DEM-DFN model
Bibliographic record
Abstract
A discrete element model (DEM) is used to study the mechanical behaviour of a pre-fractured brittle medium subjected to triaxial loadings where progressive failure can occur. The initial discrete fracture network (DFN) is based on a fractal distribution and it is used to characterize the influence of clustering and size distribution of pre-existing fractures, on the strength of fractured rock masses. The proposed approach captures the progressive failure process occurring in the rock matrix as shearing displacement takes place along pre-existing discontinuities. The results show that the mechanical behaviour of fractured rock masses is mainly dependent on the fracture intensity. However, for a given fracture intensity, the strength can exhibit a 50 per cent variability depending on the size distribution of the pre-existing fractures. This difference can be attributed to the different mechanisms that involve sliding and crushing of blocks in the case of large interconnected fractures or progressive failure of the rock matrix through coalescence of cracks in the case of small unconnected fractures. Clustering of fractures was found to influence the spatial variability of the mechanical properties and therefore to have a scale effect on strength. The results outline the relevance of three parameters, the power-law exponent of the fracture size distribution, the clustering fractal dimension which fixes the fracture-to-fracture correlation number and the fracture intensity, to characterize the mechanical behaviour of rock masses.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".