Bibliographic record
Abstract
In recent years, fiber reinforced polymer (FRP) reinforcement has garnered significant interest in the construction industry, particularly in concrete beam construction. Determining the shear capacity of FRP-reinforced beams is, however, an intricate task. The shear capacity of FRP-reinforced concrete beams can be estimated using design equations from various international standards of practice. However, these equations are often adaptations of those used for conventional steel reinforcement, resulting in unreliable estimates. This research compares design equation predictions with experimental data, utilizing test results from 48 carbon FRP-reinforced and 73 glass FRP-reinforced concrete beams. The results reveal significant differences in the accuracy and reliability of shear capacity predictions using American (ACI), Canadian (CSA), and Japanese (JSCE) design standards for both types of concrete beams. The ACI standard has high underestimation and inconsistent predictions for carbon and glass FRP beams, making it unsuitable for shear design. The CSA standard provides consistent predictions with moderate underestimation for both types of FRP beams, but it shows overestimation in certain cases. The JSCE standard consistently shows moderate underestimation without any instances of overestimation, making it a reliable tool for engineers and practitioners to estimate the shear capacity of both types of FRP-reinforced beams.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".