Responses of frozen, fine-grained soils due to multiple packets of cyclic stress with variable amplitude and an empirical prediction model
Bibliographic record
Abstract
A suite of stress-controlled cyclic triaxial tests were performed on frozen, fine-grained soils to assess the characteristics of the accumulated and cyclic behaviours under packets of cyclic stress with variable amplitude. The influence of cyclic stress sequences is highlighted, and the results of the variable amplitude cyclic tests indicate that the applied cyclic stress amplitude sequence is of importance regarding the final accumulated deformations. A step change is observed in the accumulated strain when the applied cyclic stress amplitude is beyond that of the previous stress package, while a decreasing level of cyclic stress below the previous loading package results in slight additional strain accumulation. The results of these tests also indicate that the cyclic stiffness is dependent on both the past accumulated strain and the current cyclic stress amplitude. For frozen soil, a higher past accumulated strain and lower cyclic stress levels yield a higher cyclic stiffness. An empirical approach for representing the response of frozen, fine-grained soils under multiple packets of cyclic loading is proposed and then verified by the test data. The results show that the proposed empirical model is able to extract and predict the accumulated and cyclic behaviours under multiple packets of cyclic 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".