Determination of three-dimensional shape of failure in soil slopes
Bibliographic record
Abstract
This paper presents the application of particle swarm optimization (PSO) in three-dimensional (3D) slope stability analysis to determine the shape and direction of failure as the critical slip surface. A detailed description of adopted PSO is presented and a rotating ellipsoidal shape is introduced as the possible failure surface in the analysis. Based on the limit equilibrium method, an equation of factor of safety (FoS) was developed with the ability to calculate the direction of sliding (DoS) in its internal process. A computer code was developed in Matlab to determine the 3D shape of the failure surface and calculate its FoS and DoS. Then, two example problems were used to verify the applicability of the presented code, the first by conducting a comparison between the results of the code and PLAXIS-3D finite element software and the second by re-analyzing an example from the literature to find the 3D failure surface. In addition, a hypothetical 3D asymmetric slope was introduced and analyzed to demonstrate the ability of the presented method to determine the shape and DOS of failure in 3D slope stability problems. Finally, a small-scale physical model of a 3D slope under vertical load was constructed and tested in the laboratory and the results were re-analyzed and compared with the code results. The results demonstrate the efficiency and effectiveness of the presented code in determining the 3D shape of the failure surface in soil 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".