Minimum Safety Factor Evaluation of Slopes Using Hybrid Chaotic Sand Cat and Pattern Search Approach
Bibliographic record
Abstract
This study developed an efficient evolutionary hybrid optimization technique based on chaotic sand cat optimization (CSCO) and pattern search (PS) for the evaluation of the minimum safety factor of earth slopes under static and earthquake loading conditions. To improve the sand cat optimization approach’s exploration ability, while also avoiding premature convergence, the chaotic sequence was implemented. The proposed hybrid algorithm (CSCPS) benefits from the effective global search ability of the chaotic sand cat optimization, as well as the powerful local search capability of the pattern search method. The suggested CSCPS algorithm’s efficiency was confirmed by using mathematical test functions, and its findings were compared with standard SCO, as well as some efficient optimization techniques. Then the CSCPS was applied for the calculation of the minimum safety factors of the earth slope exposed to both static and seismic loads, and the objective function was modeled based on the Morgenstern–Price limit equilibrium method, along with the pseudo-static approach. The CSCPS’s efficacy for the evaluation of the minimum safety factor of slopes was investigated by considering two case studies from the literature. The numerical experiments demonstrate that the new algorithm could generate better optimal solutions via calculating lower values of safety factors by up to 10% compared with some other methods in the literature. Furthermore, the results show that, through an increase in the acceleration coefficient to 0.1 and 0.2, the factor of safety decreased by 19% and 32%, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".