Comparison of shear strength of sand backfills measured in small-scale and large-scale direct shear tests
Bibliographic record
Abstract
Direct shear tests were conducted on 30 sand backfill materials having gravel contents ranging from 0% to 30% in a 64 mm square small-scale direct shear (SSDS) box and a 305 mm square large-scale direct shear (LSDS) box. The objectives were to compare the shearing behavior of a broad range of natural sand backfill materials tested in SSDS and LSDS and to determine if the same friction angle (φ′) is obtained in SSDS and LSDS when the natural backfill material contains gravel. Triaxial compression (TC) tests were also conducted on four of the backfill materials for comparison with the SSDS and LSDS tests. Specimens tested in SSDS and TC included only material passing the No. 4 sieve (P4). Test specimens in LSDS included the P4 material as well as material retained on the No. 4 sieve (R4), to a maximum particle diameter of 25.4 mm. Friction angles corresponding to peak strength (φ′) measured in SSDS and LSDS differed by no more than 4° for a given sand backfill, and in most cases were within 2°. The friction angles also were unaffected by removal of the R4 material. Repeatability tests showed that statistically similar failure envelopes (p-value = 0.98) are obtained in SSDS and LSDS, and that highly repeatable friction angles (φ′) are obtained using the SSDS (φ′ ± 0.25°) and the LSDS (φ′ ± 0.45°) methods. No statistically significant difference was found among the failure envelopes measured in SSDS, LSDS, and TC, suggesting that φ′ for clean sand backfill with less than 30% gravel can be measured with similar accuracy using any of the three test methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".