Framework to assess pseudo-static approach for seismic stability of clayey slopes
Bibliographic record
Abstract
Approaches commonly used to assess the seismic stability of slopes range from the relatively simple pseudo-static method to more complicated nonlinear numerical methods, e.g., finite element (FE) and finite difference (FD). The pseudo-static method, in particular, is widely used in practice as it is inexpensive and substantially less time consuming compared to the much more rigorous numerical methods. However, the pseudo-static method is widely criticized because it does not take into account the effects of the earthquake on the shear strength of the slope material nor the seismic response of the slope. Hence, some researchers recommend its use only in slopes composed of cohesive materials that do not develop significant pore pressures or that lose less than about 15% of their peak shear strength during earthquake shaking. However, the use of the pseudo-static method in these soils is also problematic as clayey slopes generally fail in pseudo-static stability analyses (i.e., factors of safety are less than 1) and the failure surface is completely predominated by the thickness of the clayey layer in the slope or foundation. The reliability of the pseudo-static method in natural clayey slopes is examined here based on rigorous numerical simulations with FLAC. The numerical results are compared and verified using available static and dynamic 1g laboratory tests. This article then addresses some of the crude assumptions of the pseudo-static method and provides practical suggestions to be applied to refine the outcomes of pseudo-static analyses not only in terms of the computed safety factors, but also in the prediction of the failure surface through the consideration of additional aspects of the dynamic responses of the clayey slopes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".