Exploring the role of sample size on fracture growth mechanisms in intact rock: insights from 3D DEM-DFN analysis
Bibliographic record
Abstract
The effect of sample size on the deformation characteristic and fracture growth mechanism of intact rock subjected to unconfined compressive stress state is examined in this study via discrete element method (DEM) coupled with discrete fracture network (DFN). Three-dimensional (3D) numerical models are first developed based on reported laboratory experiments and verified to realistically replicate the effect of sample size on the macro-mechanical response of the intact rock. This includes strength behavior, fracturing activities, and energy budgets. Micromechanical analyses are then performed to understand the role of sample size on the deformational behavior and damage progression in intact rock. Emphasis is placed on the distributions of coordination number, evolutions of crack density and the degree of crack anisotropy with regard to invariants of crack tensors. In addition, the 3D distribution of microstructure and contact networks are also presented. Results reveal that deformability and strength of intact rock are vastly reliant on the sample size. Increasing sample size facilitate the accumulation of induced microcracks due to the increased probability of interparticle bond failure. It is found that the increase in sample size can result in more tensile openings with strong dilatancy, as evidenced by local-scale porosity based on the implementation of Voronoi cells. Finally, the monotonical increase in seismic b-values with the increase in sample size suggests an acceleration in the number of small-magnitude acoustic emission (AE) events.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".