A case study and implication: particle finite element modelling of the 2010 Saint-Jude sensitive clay landslide
Bibliographic record
Abstract
Abstract Modelling of landslides in sensitive clays has long been recognised as a challenge. The strength reduction of sensitive clays when undergoing plastic deformation makes the failure proceed in a progressive manner such that a small slope failure may lead to a series of retrogressive failures and thus to an unexpected catastrophic landslide. The clay in the entire process may mimic both solid-like (when it is intact) and fluid-like (when fully remoulded, especially for quick clays) behaviours. Thereby, a successful numerical prediction of landslides in sensitive clays requires not only a robust numerical approach capable of handling extreme material deformation but also a sophisticated constitutive model to describe the complex clay behaviour. In this paper, the particle finite element method (PFEM) associated with an elastoviscoplastic model with strain softening is adopted for the reconstruction of the 2010 Saint-Jude landslide, Quebec, Canada, and detailed comparisons between the simulation results and available data are carried out. It is shown that the present computational framework is capable of quantitatively reproducing the multiple rotational retrogressive failure process, the final run-out distance and the retrogression distance of the Saint-Jude landslide. Furthermore, the failure mechanism and the kinematics of the Saint-Jude landslide and the influence of the clay viscosity are investigated numerically, and in addition, their implications to real landslides in sensitive clays are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".