Grading scalping and sample size effects on critical shear strength of mine waste rock through laboratory and in-situ testing
Bibliographic record
Abstract
Geotechnical stability analyses of mine waste rock (WR) piles require the critical friction angle ( ϕ cr ) of the coarse blasted rock. However, due to the presence of oversized rock clasts, shear strength can only be characterized on small samples prepared using grading scaling techniques, such as scalping. Thus, considering a testing device able to handle samples of characteristic size D, the material should be scaled down to a maximum particle size d max given by the minimum sample aspect ratio α = D/d max . However, a practical concern about how far the size scale can be reduced while keeping representative results remains a matter of debate in the geotechnical community. International standards do not agree on the minimum recommended α, and its effects on the mechanical behavior remain poorly understood. This paper aims to investigate the grading effects and sample size effects on ϕ cr of WR materials using the scalping technique, to provide insights on the minimum recommended α. Triaxial tests were conducted on loose and dense samples of diameters D = 150 and 300 mm. Samples were scalped from field material having d max = 75 mm, to allow a range of α from 4 to 30. Additionally, one of the world largest in-situ direct shear boxes (120 × 120 × 38 cm 3 ) was developed to test the same WR material. The results show that scalping is an appropriate technique to assess the critical shear strength of WR. The minimum α for ϕ cr assessment in triaxial testing is not sensitive to grading nor sample size, but it is affected by sample density. The aspect ratio was found to be α ≥ 12 and α ≥ 16 for loose and dense samples, respectively. This finding advocates that α values recommended by worldwide standards, such as ASTM D7181-20 , might be too low and should be revisited after comprehensive testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".