Optimization of Spline Slip Surfaces Using Metaheuristic Search in LEM
Bibliographic record
Abstract
The search for the critical slip surface on a slope is an optimization problem whereby the factor of safety is minimized over a set of parameters which define the shape of the slip surface.In limit equilibrium slope stability analysis, traditional methods for searching for the critical slip surface include grid search and auto-refine search.More recently, metaheuristic optimization methods such as Particle Swarm and Cuckoo Search, among other variations, have been used to search for critical slip surfaces.These simulate natural processes that search the solution space for a minimum solution for various optimization problems encountered in a vast range of disciplines.Typically, the parameters of spheres or ellipsoids which cut the ground topography are varied to create different slip surfaces.The parameters of cutting planes and wedges can also be varied to create multi-planar slip surfaces using the same metaheuristic techniques.However, critical slip surfaces are not always spherical, ellipsoidal, or planar in nature.This paper introduces a novel method which employs the use of three-dimensional spline surfaces in a metaheuristic search to find the critical slip surface in a slope.By varying the parameters which define the location, size and curvature of the spline, the critical slip surface can be found.The proposed formulation of parameters is shown to perform better than the parameters which define the preceding shapes due to the superior flexibility of a spline surface in its curvature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".