Probabilistic seismic slope stability analysis and design
Bibliographic record
Abstract
Deterministic seismic slope stability design charts for cohesive–frictional ([Formula: see text]) soils are traditionally used by geotechnical engineers to include the effects of earthquakes on slopes. These charts identify the critical seismic load event that is sufficient to bring the slope to a state of limit equilibrium, but they do not specify the probability of this event. In this paper, the probabilistic seismic stability of slopes, modeled using a two-dimensional spatially random [Formula: see text] soil, is examined for the first time using the random finite element method (RFEM). Slope stability design aids for seismic loading, which consider spatial variability of the soil, are provided to allow informed geotechnical seismic design decisions in the face of geotechnical uncertainties. The paper also provides estimates of the probability of slope failure without requiring computer simulations. How the design aids may be used is demonstrated with an example of slope remediation cost analysis and risk-based design.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".