Refined Approaches for Estimating the Strength of Rock Blocks
Bibliographic record
Abstract
Micro-discrete fracture networks (μDFNs) have been integrated into grain-based models (GBMs) within the numerical software UDEC to assess rock block strength through a series of unconfined compressive strength (UCS) tests of progressively larger in size numerical specimens. GBMs were generated by utilizing a Voronoi tessellation scheme to capture the crack evolution processes within the intact rock material, and μDFNs were separately created and embedded into the GBMs to simulate the effect of pre-existing defects. Various μDFNs realisations were generated stochastically within the software FracMan to assess the combined impact of defect intensity, persistence, strength and specimen size. The resulting synthetic rock block models were used to assess the “flawed” material strength at block scale through a rigorous sensitivity numerical analysis. The acquired results predict a progressive strength reduction with decreasing intact rock quality and certain trends are captured when rock block strength is expressed as a function of a newly proposed “Defect Intensity× Persistence” factor. This allow us to standardise the data along specific strength reduction envelopes and to propose generic relationships that cover a wide range of defect geometrical combinations, defect strengths and sample sizes. Accordingly, an attempt is undertaken to refine two existing empirical approaches that consider the effect of scale and micro-defects explicitly for predicting the UCS of rock blocks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".