Large-strain finite element analyses of a retrogressive landslide triggered by pile driving in sensitive clays: the case of the 1978 Rigaud landslide in Québec
Bibliographic record
Abstract
A retrogressive landslide triggered by pile driving in sensitive clays in Rigaud, Quebec, is analyzed. The article presents the landslide characteristics, post-failure assessments, and potential failure mechanisms. To gain deeper insights into the triggering and propagation of failure, large-strain finite element (FE) modelling was conducted using a Eulerian-based FE approach. The FE simulations reveal that pile installation can induce localized shear band formation, ultimately leading to a large-scale landslide without the need for additional external loading. Key factors influencing the failure pattern include soil stratification, sensitivity, and the rate of post-peak shear strength degradation. The numerical modelling effectively replicates the observed field behaviour of the landslide, capturing crucial aspects such as retrogression distance, failure pattern, and the downslope displacement of failed soil masses. Although the landslide involved complex three-dimensional effects and triggering conditions, the current two-dimensional large-strain FE simulations under plane strain conditions provide valuable insights into the underlying progressive failure mechanisms—insights that cannot be obtained using traditional limit equilibrium and conventional FE analyses. These findings underscore the significant role of pile driving in initiating landslides in sensitive clays and highlight the necessity of advanced numerical approaches to accurately predict and mitigate such failures.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".