An innovative multi-objective optimization approach for compact concrete-filled steel tubular (CFST) column design utilizing lightweight high-strength concrete
Bibliographic record
Abstract
Incorporating sustainability into Concrete-Filled Steel Tubular (CFST) columns' optimization can enhance efficiency and sustainability in construction. Discrepancies in international standards for ultimate load capacity computation in compact CFST columns under eccentric loading, particularly with lightweight high-strength concrete, pose challenges. This research compile a dataset of compact CFST columns, evaluating design codes (AISC 360-16, Eurocode 4) against experimental results. Besides, a comprehensive finite-element model predicts compact CFST column performance, investigating axial force-moment (P-M) interaction behavior with respect to the material strength ratio (fy/fc′). In the second phase of the study, an ANN model, incorporating input parameters, estimates axial load capacity, facilitating multi-objective optimization for optimal CFST column geometry. The results confirmed that Eurocode 4 outperforms AISC 360-16 in experimental axial capacity predictions (Nuc/Nuc,theoretical) where, the mean and standard deviation for Eurocode 4 were estimated at 1.07 and 0.22, respectively, compared to 1.21 and 0.29 for AISC 360-16. Besides, statistical metrics confirm the precision of the ANN model, particularly with high-strength concrete, promising efficiency in future computational intelligence-based structural design platforms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".