Downhill progressive landslides in long natural slopes: triggering agents and landslide phases modeled with a finite difference method
Bibliographic record
Abstract
A large landslide in Tuve (Gothenburg, Sweden, 1977) initiated the development of a model for slope stability analysis taking the deformation-softening of soft sensitive clays into consideration. The model studies triggering agents and five phases in progressive slope failure are identified: (1) in situ, (2) disturbance, (3) unstable “dynamic”, (4) transitory (or permanent) equilibrium, and (5) “global” failure. The clay resistance in these phases may differ widely; mostly due to different rates of loading. Two time-dependent failure criteria are defined: (i) the triggering load condition in the disturbance phase 2 and (ii) the transitory equilibrium in phase 4, indicating whether minor downhill displacements or a veritable landslide catastrophe will occur. The analysis explains why downhill landslides tend to spread over vast areas of almost horizontal ground further downslope. The model has been applied to landslides in Scandinavia and Canada. Three case studies are briefly discussed. The model is a finite difference approach, where local downhill deformations caused by normal forces is maintained compatible with deviatory shear deformations above — and, if relevant, below — the potential (or the established) failure surface. Software and an easy-to-use spreadsheet are introduced as well as recent developments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".