Stability and failure mass of unsaturated heterogeneous slopes
Bibliographic record
Abstract
Rainfall infiltration in an unsaturated soil slope induces loss of suction (and even positive pore-water pressures), which can eventually lead to failure. This paper investigates the probability and the size of failure of an unsaturated slope with spatially variable void ratio, subjected to a constant intensity rainfall. The random finite element method is employed in conjunction with a Monte Carlo simulation to stochastically evaluate the factor of safety and the size of the sliding mass. The results indicate that the mean value and the variability of these two quantities depend on both correlation length and coefficient of variation of the void ratio field. This dependency is more prominent during the transient regime than at steady states. Notably, the factor of safety in some cases can be low but the corresponding sliding mass is relatively small, while in other instances, the factor of safety might remain large though the associated sliding mass is very sizeable. The correlation between the factor of safety and the size of the sliding mass shifts from positive to negative as the rainfall progresses. A simple quadrant plot is suggested to assess the risk associated with slope failure, taking into account both the factor of safety and the size of failure, rather than the factor of safety alone as it is usually the case. The study also demonstrates an application of a numerical approach to assess stability of geostructures composed of complex multiphase materials such as unsaturated soils or frozen soils.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".