Bond Behavior of Glass Fiber-Reinforced Polymer (GFRP) Bars Embedded in Concrete: A Review
Bibliographic record
Abstract
Glass Fiber-Reinforced Polymer (GFRP) bars are becoming increasingly common in structural engineering applications due to their superior material properties, mainly their resistance to corrosion due to their metallic nature in comparison to steel reinforcement and their improved durability in alkaline environments compared to CFRP and BFRP reinforcement. However, GFRP bars also suffer from a few limitations. One of the main issues that affects the performance of GFRP reinforcing bars is their bond with concrete, which may differ from the bond between traditional steel bars and concrete. However, despite the wide attention of researchers, there has not been a critical review of the recent research progress on bond behavior between GFRP bars and concrete. The objective of this paper is to provide an overview of the current state of research on bond in GFRP-reinforced concrete in an attempt to systematize the existing scientific knowledge. The study summarizes experimental investigations that directly measure bond strength and investigates the different factors that influence it. Additionally, an overview of the analytical and empirical models used to simulate bond behavior is then presented. The findings indicate the dependence of the bond on several factors that include bar diameter, bar surface, concrete strength, and embedment length. Additionally, it was concluded that both traditional and more recent bond models do not explicitly account for the effect of different factors, which highlights the need for improved bond models that do not require calibration with experimental tests.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".