Slender Steel Columns Strengthened Using High-Modulus CFRP Plates for Buckling Control
Bibliographic record
Abstract
This paper presents the results of an experimental investigation into the behavior of slender steel columns strengthened using high-modulus (313GPa), carbon fiber-reinforced polymer (CFRP) plates. Eighteen slender hollow structural section square column specimens, 44×44×3.2mm, were concentrically loaded to failure. The effectiveness of CFRP was evaluated for different slenderness ratios (kL∕r), namely, 46, 70, and 93. The maximum increases in ultimate load ranged from 6 to 71% and axial stiffness ranged from 10 to 17%, respectively, depending on kL∕r. As kL∕r reduced, the effectiveness of CFRP plates also reduced, and failure mode changed from CFRP plate crushing after occurrence of overall buckling, to debonding prior to, or just at, buckling. A simplified analytical model is proposed to predict the ultimate axial load of FRP-strengthened slender steel columns, based on the ANSI/AISC 360-05 provisions, which were modified to account for the transformed section properties and a failure criteria of FRP derived from the experimental results. It was shown that for a given FRP reinforcement ratio, there is a critical kL∕r at the low end, below which FRP may not enhance the strength of the column.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".