Three-dimensional slope stability based on stresses from a stress-deformation analysis
Bibliographic record
Abstract
A procedure is developed where stresses from a finite element analysis are incorporated into a limit equilibrium framework to evaluate the stability of three-dimensional slopes. An independent stress-deformation analysis is performed to calculate the internal stress state for the slope. The stress distribution is imported into the three-dimensional slope stability analysis in the form of a regular grid. The slip surfaces considered in the limit equilibrium analysis are ellipsoidal and discretized using a series of triangular planes. The normal and shear force acting at the centroid of individual triangular planes can be computed from the internal stress distribution. Subsequently, the factor of safety of a selected slip surface can be calculated directly without using an iterative procedure. A series of verification examples are presented to confirm that the proposed method provides the required accuracy and flexibility to assess the stability of slopes typically encountered in practice. Sensitivity analyses are presented to show how the procedure used to compute the forces acting on each triangular plane, the number of planes used to discretize the slip surface, and Poisson’s ratio influence the computed factors of safety, but do not limit the successful application of the methodology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".