Experimental Study on the Minimum Required Specimen Width to Maximum Particle Size Ratio in Direct Shear Tests
Bibliographic record
Abstract
Conducting laboratory direct shear tests on granular materials is a common practice in geotechnical engineering. This is usually done by following the ASTM D3080/D3080M-11 (hereafter named ASTM), which stipulates a minimum required value of 10 for specimen width (W) to the maximum particle size (dmax) ratio. Recently, a literature review performed by the authors showed that the minimum required W/dmax ratio given in the ASTM is not large enough to eliminate the specimen size effect (SSE). The minimum required W/dmax ratio of ASTM needs to be revised. In this study, a critical analysis is first made on existing data in order to identify the minimum required W/dmax ratio. The analysis shows that more experimental data obtained on specimens having W/dmax ratios between 10 and 50 are particularly necessary. To complete this need, a series of direct shear tests were performed on specimens having different dmax by using three shear boxes of different dimensions. The results show once again that the minimum required W/dmax ratio of 10, defined in the ASTM, is not large enough to eliminate the SSE. Further analysis on these and existing experimental results indicates that the minimum required W/dmax ratio to remove the SSE of friction angles is about 60. These results along with the limitations of this study are discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".