A simplified method to predict failure of sands under general cyclic simple shear loading
Bibliographic record
Abstract
This paper describes a simplified approach based on constant-volume cyclic simple shear (CSS) tests with uniform sinusoidal loading that can predict failure of dry sands under general shear stress–time histories. The simplified method is based on the cumulative energy hypothesis that states that the dissipated energy required by a sand sample to reach failure depends only on its initial state (D r and [Formula: see text]) and is independent of the characteristics of the cyclic loading applied. The proposed method uses a sand-specific multivariable regression developed using a small number of CSS tests involving uniform sinusoidal loading without the need for advanced general cyclic loading tests. Furthermore, the regression requires only a small data set involving one uniform CSS test per sample initial state. The simplified method was evaluated using two comprehensive experimental studies involving two different test sands. The first data set is an experimental programme by the authors involving 20/30 Ottawa sand subjected to different cyclic loading types. The second data set is an independent experimental programme that used 0/30 Monterey sand. In both cases, the simplified approach was found to yield reasonable predictions of failure of the test sands when subjected to complex and irregular shear stress loading.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".