Deflection and Cracking of Fiber-Reinforced Self-Consolidated Concrete Beams Reinforced with GFRP Bars under Cyclic Loading
Bibliographic record
Abstract
Deflection and crack width under service loads are crucial factors that dictate the flexural design of elements reinforced with glass fiber–reinforced polymer (GFRP) bars, primarily due to their relatively low elastic modulus. This study investigated the impact of incorporating fibers on the serviceability performance of self-consolidating concrete (SCC) beams reinforced with GFRP bars. Eight full-scale concrete beams—each measuring 3,100 mm in length, 200 mm in width, and 300 mm in height—were fabricated and tested until failure under four-point bending cyclic loading. Six specimens were cast using fibers, while the other two were cast with normal SCC as reference specimens. The test parameters included polypropylene (PP) fiber volume (0%, 0.5%, and 0.75%), fiber combination (macro PP fibers and a mix of macro PP and micro basalt fibers), and longitudinal reinforcement ratio (0.78% and 1.66%). The test results revealed that increasing PP fiber volume and reinforcement ratio improved the serviceability and flexural performance of the beams under cyclic loading by effectively restraining crack width and reducing deflections at both service and ultimate limit states. Furthermore, combining micro basalt fibers with macro PP fibers notably enhanced the serviceability parameters of beams with both low and high GFRP reinforcement ratios, surpassing beams reinforced solely with macro PP fibers. A theoretical prediction per North American codes and design guidelines was conducted, including deflection and crack width, and these results were subsequently compared to the experimental findings.
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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".