New methods for system reliability analysis of soil slopes
Bibliographic record
Abstract
A slope may have many possible slip surfaces. As sliding along any slip surface can cause slope failure, the system failure probability of a slope is different from the probability of failure along an individual slip surface. In this paper, we first suggest an efficient method for evaluating the system failure probability of a slope that considers a large number of possible slip surfaces. To obtain more insights into the system failure probability of a slope, we also propose a method to identify a few representative slip surfaces most important for system reliability analysis among a large number of potential slip surfaces and to calculate the system failure probability based on these representative slip surfaces. An equation for estimating the bounds of system failure probability based on the failure probability of the most critical slip surface is also suggested. The system failure probability is governed by only a few representative slip surfaces. For a homogenous slope, the failure probability of the most critical slip surface is a good approximation of the system failure probability. For a slope in layered soils, the system failure probability can be significantly larger than the failure probability of the most critical slip surface.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".