Direction-dependent failure envelopes of sand-structure interfaces with snakeskin-inspired surfaces
Bibliographic record
Abstract
Snakeskin-inspired surfaces generate direction-dependent interface strengths, with shearing in the cranial direction (i.e.; against the scales) generating greater strength than in the caudal one (i.e.; along the scales). This directionality is enabled by the transfer of load in friction and passive resistances. It is unclear if failure of these interfaces can be captured by models used for purely frictional interfaces. Interface shear tests complemented with particle image velocimetry (PIV) analyses were performed on snakeskin-inspired surfaces with different asperity geometries and on reference rough and smooth surfaces. The results of experiments performed at different initial effective stresses under constant normal stiffness boundary conditions show significant dilation-induced increases in effective stress. These increases were greater during cranial than caudal shearing, producing greater cranial strengths. These surfaces yielded nonlinear failure envelopes, with greater shear to effective stress ratios at smaller effective stresses, while the rough and smooth surfaces mobilized linear failure envelopes. The PIV analyses indicate that the snakeskin-inspired surfaces induce localized strains in the vicinity of the asperities, leading to wavy failure planes. The average shear strains are correlated with the mobilized shear strengths, with increases in effective stress leading to decreases in both strains and stress ratio.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".