Optimization of Concrete-Filled Steel Tubular (CFST) Columns Using Meta-Heuristic Algorithms
Bibliographic record
Abstract
Concrete-filled steel tubular (CFST) columns are an extensively studied area due to the favorable structural characteristics of these members. In order obtain the best possible performance from these structures while reducing the cost the use of optimization algorithms is indispensable. For this reason, meta-heuristic algorithms are finding increasing application in engineering due to their high efficiency. Various equations that predict the axial ultimate load-carrying capacity (Nu) of CFST columns are available in design codes as well as the research literature. However, most of these equations are only applicable within certain parameter ranges. A recently developed set of equations that have better parameter ranges of applicability was applied in this study. Furthermore, a newly developed meta-heuristic algorithm called social spider algorithm is applied to the cross-section optimization of circular CFST columns. The improvement of the structural dimensioning under Nu constraint was demonstrated.
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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) | 0.000 | 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".