Strengthening Long Steel Columns of S-Sections against Global Buckling around Weak Axis Using CFRP Plates of Various Moduli
Bibliographic record
Abstract
The traditional Euler’s buckling theory of slender columns indicates that column capacity depends on flexural rigidity (EI), rather than material strength. As such, the availability of ultrahigh modulus carbon fiber-reinforced polymer (CFRP) plates, which could be much stiffer than steel, can offer a unique alternative for strengthening slender steel columns, in lieu of welding or bolting steel plates. In this study, twelve 2.6 m long S75×8 steel columns of 197 slenderness ratio that represents the upper limit permitted by code were tested under concentric axial loading using pin-ended conditions. The columns were allowed to buckle around their weak axes. CFRP plates were adhesively bonded to the flanges of the steel I-shape sections in nine of the columns. The main parameters studied were the level of initial out-of-straightness [length(L)/8,387 to L/1,020], CFRP modulus (168–430 GPa), CFRP reinforcement ratio (13–34%) and the length of CFRP plate (33–95% of L). The gain in axial strength due to CFRP retrofitting ranged from 11 to 29%, depending on the various parameters. The gain generally increased as CFRP modulus, initial out-of-straightness, or CFRP reinforcement ratio increased. Global buckling consistently governed the maximum load. In the case of the 430 GPa CFRP, buckling was followed by CFRP crushing in compression, then rupture in tension.
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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) | 0.001 | 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) | 0.002 | 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".