Investigating the influence of macro-synthetic fibers on shear strength of GFRP-RC beams
Bibliographic record
Abstract
Incorporating macro-synthetic fibers can mitigate the reduced shear resistance due to the lower modulus of elasticity in glass fiber-reinforced polymer (GFRP) bars. This research presents an experimental and analytical investigation of the shear capacity of GFRP-reinforced concrete beams with macro-synthetic fibers. Eight GFRP reinforced concrete beams with and without GFRP stirrups, measuring 4350 × 400 × 250 mm, were subjected to a four-point loading test. This study assessed the impact of three volume fractions of macro-synthetic fibers (0 %, 0.125 %, and 0.25 %), flexural reinforcement ratios (0.72 % and 1.13 %), and the presence of GFRP stirrups. The results indicate that the inclusion of macro-synthetic fibers significantly increased the number of cracks with a more uniform stress distribution, increased the initial cracking load, and enhanced the ultimate shear capacity. Specifically, the inclusion of 0.125 % and 0.25 % fiber volumes increased the initial cracking load by 13.5 % and 22.9 %, respectively, while improving the ultimate shear capacity by 14.5 % and 27.4 %, compared to beams without fibers. The experimental shear resistance was compared with the predictions of the ACI 440.11–22, CSA S806–12, CSA S6–19, and ACI 544.4R-18 shear equations to evaluate their accuracy. Furthermore, literature and current tested specimens were employed to validate the proposed shear equation for predicting the ultimate shear strength of GFRP-fiber reinforced concrete (FRC) beams. The average ratio of the experimental to predicted shear capacity ( V exp / V pred ) for the beams was 1.03, with a coefficient of variance of 18.1 %.
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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) | 0.000 | 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".