A novel displacement-based rigorous limit equilibrium method for three-dimensional landslide stability analysis
Bibliographic record
Abstract
Conventional stability analysis of landslides is investigated using the factor of safety of the entire sliding body, which provides no information concerning displacements of the analyzed landslides. In this paper, a novel displacement-based rigorous limit equilibrium method is proposed to investigate the displacements and stabilities of three-dimensional landslides. The relationship between the shear stresses acting on the base of the columns and the shear displacements is established based on the hyperbolic soil model, which can be directly obtained from direct shear tests. According to the displacement compatibility among the columns, the shear displacements of all columns can be determined by the vertical and horizontal displacements at a key point. Combining the six equilibrium conditions of the discretized columns with the nonlinear constitutive relation between stress and displacement of soils, the vertical displacement at the key point can be determined. By introducing the strength reduction technique into the displacement-based rigorous limit equilibrium method, the relationship between the reduction factor and the vertical displacement can be obtained. The displacement and the safety factor of three-dimensional landslides can be defined. Moreover, two cases are given to verify the robustness and precision of the present method in detail.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".