Determination of the Shear Strength of Rockfill from Small‐Scale Laboratory Shear Tests: A Critical Review
Bibliographic record
Abstract
Determining the shear strength of rockfill is a key task for the design and stability analysis of rockfill structures. When direct shear tests are performed, the well‐established ASTM standard requires that specimen width and thickness must be at least 10 and 6 times the maximum particle size ( d max ), respectively. When the value of d max is very large, performing such tests in laboratory with field rockfill becomes difficult or impossible. Four scaling‐down techniques were proposed in the past to obtain a modeled sample excluding oversize particles: scalping, parallel, replacement, and quadratic. It remains unclear which of the four scaling‐down techniques yields reliable shear strength of field rockfill. In this paper, an extensive review is presented on existing experimental results to analyze the capacity of each scaling‐down technique to determine the field rockfill shear strength. The analyses show that previous researches followed an inappropriate methodology to validate or invalidate a scaling‐down technique through a direct comparison between the shear strengths of modeled and field samples. None of the four scaling‐down techniques was shown to be able or unable to predict the field rockfill shear strength by extrapolation. The analyses further show that the minimum ratios of specimen size to d max dictated by well‐established standards are largely used but are too small to eliminate the specimen size effect. In most cases, this practice results in shear strength overestimation. The validity or invalidity of scaling‐down techniques based on experimental results obtained by using the minimum ratios is uncertain. Recommendations are given for future studies.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".