An exponential model for strain softening behavior of sensitive clays
Bibliographic record
Abstract
Abstract Strain softening in sensitive clays is a major cause of retrogressive landslides. The assessment of post failure movements like retrogression or run out in such landslides requires detailed data regarding the post peak parameters, especially in terms of stress and strain at remoulded state. The limitations concerning experimental studies in this regard is well known which has often led to the use of mathematical and analytical models in assessing strain softening. Here, an exponential model to predict strain softening is proposed by making use of triaxial testing data. The model is developed through a series of triaxial testing results collected from ten different sites in Eastern Canada. The developed softening equation is governed by the peak undrained shear strength, sensitivity of the clay, ease of strength reduction from the peak to the remoulded state and the strain at remoulded strength. The main advantage is that a quick and reasonable evaluation of the softening behaviour of the sensitive clay maybe carried out through experimental studies. The prediction of strain at remoulded state is an important outcome of this study and is consistent with field data. Keeping in mind the effect of geological and topographical factors in the estimation of post failure movements in retrogressive landslides, an attempt has been made to conduct a preliminary assessment of the retrogression distance through the strain at remoulded state.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".