Predicting axial load capacity of <scp>CFST</scp> columns using machine learning
Bibliographic record
Abstract
Abstract Owing to their economic and structural advantages, concrete‐filled steel tubular (CFST) columns have been implemented in diverse structural applications, especially in high‐rise buildings, suspension bridges, and subway stations. However, there is no agreement between international standards regarding the ultimate compressive strength of CFST columns subjected to concentric axial force or combination of bending moment and axial force, especially for slender sections and high‐strength materials. Considering such limitations, a comprehensive dataset on rectangular and circular (built up and hot rolled) CFST sections with different slenderness ratios was collected. The compiled experimental results were compared with corresponding values calculated by pertinent design code provisions, including AISC 360‐16 and Eurocode 4. The accuracy of design codes in predicting the axial capacity of CFST sections under concentric and eccentric loading was assessed accordingly. The distribution of the experimental‐to‐predicted data confirmed that the prediction error was dependent on the slenderness ratio of columns. However, the effects of other parameters, including the mechanical properties of materials and eccentricity level on the model error, were negligible. In the second phase of the study, a surrogate Machine‐Learning (ML) model was developed to estimate the axial capacity of circular and rectangular CFST columns under centric or eccentric loading condition. The accuracy of the proposed ML predictive model was appraised using several statistical metrics. The novel informational model attained superior accuracy and could be used to simplify generative design in future computational intelligence 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".