Failure mode transitions of unconfined granular media from dry to unsaturated to “quasi-fully” saturated states
Bibliographic record
Abstract
Recent research has shown discrepancies between the prevailing mathematical representations of near-surface shear strength and the observed shear strengths. This investigation focuses on three granular materials, i.e., 1) poorly-graded, medium-fine silica-quartz sand, 2) an engineered silica-quartz mix of 3.38-mm and 0.638-mm sub-angular particles, and 3) an angular fused quartz sand. Specimens were tested under load-controlled conditions at variable saturations in order to identify and quantify the influence of suction on the granular structures and failure modes. All three materials exhibited localized radial particle force chain buckling failures in unconfined drained dry (UDκ) conditions and classical shear failures in the unconfined drained unsaturated shear (UDP) conditions. In unconfined drained suction failures (UDS) conditions, the poorly-graded, medium-fine silica-quartz sand exhibited a bulging and sloughing failure without weeping, while the other two materials wept and then held loads before failure. Thus, it is suggested that the pore fluid had a predominate lubrication (strength weakening) effect, and the assumption of structure stiffening (strength increase) from matric suction may not be valid at near-surface conditions for sub-angular silica-quartz materials but is valid for the angular fused quartz.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".