Study on Stability of High Embankment with Strong Weathering Filling under Complicated Stress Conditions
Bibliographic record
Abstract
Strength reduction elasto-plastic finite element analysis defines the reduction factor when slope has been destroyed as the slope stability factor of safety, which combines with strength reduction technique, the limit equilibrium theory and the principle of elastic-plastic finite element. Three-dimensional finite element analysis should be used in stability analysis of slope because it can overcome the short advantages of two-dimensional finite element and can simulate the complex topographic and geological conditions. Based on the large-scale triaxial shear test, the modified Duncan-Chang model is established. Based on strength reduction elasto-plastic finite element, stability of high fill embankment was studied with three-dimensional finite element method considering the complex terrain conditions. Study results suggest that plastic strain and displacement mutant of slip surface node can be a sign of slope instability as a whole. At the same time calculation of three-dimensional finite element also does not converge. Therefore, it can be slope instability criterion calculate whether the finite element static analysis converges or not. On the other hand, stability safety factor of high fill embankment under three-dimensional conditions is larger than that of two-dimensional conditions, which shows that boundary conditions of high fill embankment enhance its stability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".