Reliability assessment of slopes with three-dimensional rotated transverse anisotropy in soil properties
Bibliographic record
Abstract
The influence of soil variability on three-dimensional (3D) probabilistic slope stability analysis has been investigated previously for soils that display isotropic spatial variability features or anisotropic horizontal fabric patterns. However, due to various soil deposition processes, weathering, filling or tectonic movements, the assumptions of isotropy or horizontal layering may not always be realistic. This study presents 3D analyses of slopes with spatially variable soils associated with rotated transverse anisotropy features. The results show that for cross-dip slopes where the strike direction of soil strata is perpendicular to the out-of-plane direction of the slope, the reliability depends on various factors including strata rotation angle and autocorrelation distances, and differs significantly from slopes with horizontally deposited soil fabric. The influence of strata orientation is also pronounced for dip slopes and reverse dip slopes, and these are presented in terms of reliability indices of the slopes and statistics of the length of sliding mass and elaborated by considering the failure mechanism under different scenarios. Through these analyses, this paper discusses the key features of slope reliability considering rotated transverse anisotropy in soil properties, and their major differences from situations involving horizontal soil layers or two-dimensional probabilistic assessments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".