Experimental study on shear behavior of carbon-fiber-reinforced polymer reinforced concrete short beams without web reinforcement
Bibliographic record
Abstract
Recent literature emphasized the scarcity of information on the shear behavior of fiber-reinforced polymer (FRP) reinforced concrete short beams and the need to develop sufficient experimental data in this area. The present study responds to this need by conducting shear force testing on eight concrete short beams reinforced with carbon-fiber-reinforced polymer (CFRP) and four control concrete beams reinforced with steel. To ensure a shear failure, all tested beams were reinforced with only bottom longitudinal reinforcement and no web reinforcement was provided. The crack pattern, reinforcement strain, mode of failure, and shear strength and deflection of tested beams were studied. The influence of the shear span to effective depth ratio, a/d, beam effective depth, d, longitudinal reinforcement ratio, ρ, and concrete compressive strength, f ′con the shear behavior of CFRP-reinforced concrete short beams was examined. It was observed that the experimental parameters investigated had a significant effect on the shear strength and deflection of tested beams. It was also found that the strut-and-tie method more accurately predicts the shear strength of steel-reinforced concrete short beams than it does for similar CFRP-reinforced beams and, thus, needs to be modified to be applicable for reinforced concrete beams with FRP reinforcement.
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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 | 0.001 | 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".