Influence of CFRP Thickness, Concrete Strength, and Reinforcement Detailing on the Cyclic Performance of RC Beams
Bibliographic record
Abstract
CFRP sheet strengthening has been widely adopted as an effective technique for enhancing the cyclic performance of reinforced concrete (RC) structures.However, variations in CFRP thickness, concrete strength, and steel reinforcement detailing can significantly influence the ductility and energy dissipation of strengthened beams.This study investigates the cyclic response of six full-scale rectangular RC beams subjected to four-point bending flexurally strengthened with CFRP sheet.The beams are categorized into two groups, each consisting of three specimens: a control beam, a beam strengthened with a thin CFRP sheet, and a beam strengthened with a thick CFRP sheet.Group I beams are constructed with No. 13 longitudinal steel and concrete compressive strength of 17.2 MPa, while Group II beams utilize No. 16 longitudinal steel and a higher concrete compressive strength of 31 MPa.Key parameters, including load-deflection behavior, energy dissipation, and ductility, are compared across the two groups.Experimental results highlight the impact of CFRP thickness, concrete strength, and steel reinforcement on RC beam cyclic behavior.Experimental results show that thin CFRP sheets offer a better balance between strength and ductility, enabling higher energy dissipation.Conversely, thick CFRP sheets increase load capacity but reduce ductility due to early debonding.Beams with lower-strength concrete and smaller reinforcement exhibited greater ductility and energy absorption, while higher-strength concrete and larger reinforcement enhanced stiffness and ultimate load capacity.
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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) | 0.000 | 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".