Optimized calculation of bearing capacity of short columns of steel-tube-concrete hybrid structures combined with genetic algorithm
Bibliographic record
Abstract
This research proposes a new optimization technique for reinforcement concrete filled structural tubular columns using genetic algorithms and unified strength theory.A complete theoretical model to determine the axial bearing capacity of reinforced CFST columns incorporating modified confinement coefficients and enhanced steel section properties was developed.The optimization procedure deals with performance of the structure, materials usage and construction convenience as the optimization goals.Experimental validation for ultimate bearing capacity of five full scale specimens was carried out and the deviation was found out to be 5.2% which was found to be predicted by the theoretical model accurately.Internal stiffeners are likely to increase axial capacity by about 15.7%-23.4% over traditional CFST columns.The relationship between stiffener parameters and performance of the structure was found to be critical with optimal height to thickness of the stiffener to be in the range of 30 to 45 and space to diameter ratio no greater than 0.5.The problem sets out such mathematics as is nowadays simply necessary for the modern construction world to have at their disposal, as well as reasons for designing reinforced CFST columns.
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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) | 0.001 | 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".