Enhancing buckling capacity of slender s-section steel columns around strong axis using bonded carbon fibre plates
Bibliographic record
Abstract
This paper investigates a strengthening technique of slender S-section steel columns against global buckling about their strong axes. Carbon fibre-reinforced polymer plates of various Young’s moduli, ranging from 168 to 430 GPa, were adhesively bonded to the steel flanges. The number of carbon fibre-reinforced polymer layers was varied to produce reinforcement ratios of 11–34%. The study comprised eight 2.6 m long pin-ended columns with a slenderness ratio of 83, including five strengthened with carbon fibre-reinforced polymer. The out-of-straightness imperfection of the columns ranged from length (L)/29,000 to L/1600. It was shown that the increase in strength ranged from 15% for the smallest out-of-straightness to 25% for the largest. Increasing reinforcement ratio of the 168 GPa modulus carbon fibre-reinforced polymer from 11 to 34% increased strength from 5 to 15%. Although a higher modulus carbon fibre-reinforced polymer would generally be favoured for achieving higher gains in buckling strength according to Euler’s buckling theory, this study showed that failure strain of such carbon fibre-reinforced polymer could be a critical limiting factor. It is important that global buckling, and hence the peak load, is achieved before carbon fibre-reinforced polymer crushing.
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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 | 0.000 |
| 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) | 0.001 | 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".