Modelling seasonal ratcheting and progressive failure in clay slopes: a validation
Bibliographic record
Abstract
Seasonal wetting and drying stress cycles can lead to long-term deterioration of high-plasticity clay slopes through the accumulation of outward and downward deformations leading to plastic strain accumulation, progressive failure, and first-time failures due to seasonal ratcheting. Using recent advances in hydromechanical coupling for the numerical modelling of unsaturated soil behaviour and development of nonlocal strain-softening regulatory models to reduce mesh dependency of localization problems, the mechanism of seasonal ratcheting has been replicated within a numerical model. Hydrogeological and mechanical behaviours of the numerical model have been compared and validated against physical measurements of seasonal ratcheting from centrifuge experimentation. Following validation, the mechanism of seasonal ratcheting was explored in a parametric study investigating the role of stiffness and long-term behaviour of repeated stress cycling extrapolated to failure. Material stiffness has a controlling influence on the rate of strength deterioration for these slopes; the stiffer the material, the smaller the seasonal movement and therefore the more gradual the accumulation of irrecoverable strains and material softening. The validation presented provides confidence that the numerical modelling approach developed can capture near-surface behaviour of high-plasticity, overconsolidated clay slopes subject to cyclic wetting and drying. The approach provides a tool to further investigate the effects of weather-driven stress cycles and the implication of climate change on high-plasticity clay infrastructure slopes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".