An efficient approach for locating the critical slip surface in slope stability analyses using a real-coded genetic algorithm
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Bibliographic record
Abstract
A real-coded genetic algorithm is employed to develop a search approach for locating the noncircular critical slip surface in slope stability analyses. Limit equilibrium methods and the finite-element-based method are incorporated with the proposed search approach to calculate the factor of safety. Geometrical and kinematical compatibility constraints are established based on the features of slope problems to prevent slip surfaces from being unreasonable. A dynamic bound technique is presented to improve the search performance with more effective exploration within the solution domain. A number of examples are investigated that demonstrate the proposed search approach to be efficient in yielding accurate solutions to practical slope stability problems. The proposed search approach is stable and highly correlated with the results of independent analyses. Furthermore, this paper demonstrates the successful application of a real-coded genetic algorithm to noncircular critical slip surface search problems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it