Suggested methods for estimation of confined strength of heterogeneous (defected) rocks
Bibliographic record
Abstract
A grain-based model based on the distinct element method previously calibrated to the laboratory properties of intact and heat-treated (granulated) marble is used to simulate grain-scale heterogeneities, such as grain boundary cracks and laboratory specimen-scale heterogeneities (often referred to as defects), such as veins. The semi-empirical Strength Degradation Approach (SDA), originally developed for the estimation of the confined strength of crack-damaged rocks, is first applied and tested for the estimation of confined strength of laboratory specimen-scale rocks and rock blocks containing defects. In the second approach, called the explicit numerical modelling approach, the grain-based model is integrated with discrete fracture network (DFN) models, to construct defected rock models and to investigate the influence of defect orientation on the confined strength of defected rocks. It is concluded that the SDA can be used at the early stages of geotechnical projects, when limited information on the properties and geometrical characteristics of defects is available. The explicit numerical modelling approach provides more representative results than the SDA, as it requires a detailed knowledge of the properties of intact rock as well as the properties and geometrical characteristics of defects obtained from field mapping and laboratory testing. A step-by-step procedure is provided for the application of these two methods to estimate the confined strength of defected rocks.
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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 | 0.002 |
| 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 | 0.000 |
| 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".