Integrating Eurocode 7 (load and resistance factor design) using nonconventional factoring strategies in slope stability analysis
Bibliographic record
Abstract
In traditional allowable stress design, as known, the safety factor is calculated with respect to shear strength of soil(s) by dividing the available shear strength by the mobilized stresses. The limit-state method, on the other hand, compares — in the form of the inequality Ed≤ Rd— the effects of all the actions, Ed, with the corresponding resistance of the ground, Rd. Although this method considers different loading conditions by using suitable combinations of design values, it is still based on direct comparison of the available shear strength with the mobilized stresses. In the present paper, various factoring strategies (in addition to the traditional one with respect to shear strength of soils) are integrated into a limit-state method framework. Eurocode 7 has been chosen for this purpose. The whole procedure aims at giving a more comprehensive insight into the design of slopes and the sensitivity of safety level of slopes to the various parameters. In addition, the proposed methodology, as shown, may result in a safety level of slopes significantly lower than the respective one obtained using the limit-state method in its traditional form. As man-made slopes that conform to design standards often fail in practice, even though conservative input values are used, these failures must be treated with more skepticism by practitioners adopting supplementary design practices such the one presented herein.
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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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".