Geotechnical centrifuge modelling of retrogressive sensitive clay landslides
Bibliographic record
Abstract
Sensitive clay landslides represent a significant geohazard due to their well-known potential for extensive retrogressive failures, on the scale of multiple hectares, which could encompass surrounding communities and infrastructure. Interpretation of retrogression mechanisms is often limited as only forensic investigations are possible. This work presents the results of a physical modelling study to examine retrogressive failures, analysis of each failure episode, and interpretation of the results using published relationships. Five novel centrifuge model tests were conducted under a defined range of undrained shear strength and slope angle conditions. The models are constructed of a sensitive cement–soil mixture that allows for a consistent contractile material with bespoke shear strength. Results indicate the observed retrogression distance correlates with Taylor’s stability number. The addition of a 5° slope angle to invoke a static shear stress on the model provoked notably larger retrogression distances. Post-test undrained shear strength measurements quantified softening of the material along the failure surface. Stability analyses on each failure episode captured the observed failure geometry and factor of safety. Results indicate that the geometric parameters of a slope, specifically the slope angle, may be able to explain a component of the scatter for relating Taylor’s stability number with retrogression distances.
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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 | 0.001 | 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 | 0.000 |
| 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".