Durability of GFRP bars: a critical review of the literature
Bibliographic record
Abstract
Abstract The research undertaken during the last two decades has shown that one of the potential solutions to the steel‐corrosion‐related problems in concrete is the use of fiber‐reinforced composite (FRP) reinforcement as a replacement for traditional steel bars. Glass FRP (GFRP) reinforcement is gaining more popularity in construction of bridges and in other concrete structures because of its low cost compared to Carbon FRP reinforcement. The durability of these materials, especially under severe environmental conditions, is now recognized as the most critical topic of research. The lack of data on durability of the GFRP reinforcements is a major obstacle to their acceptance on a broader scale in civil engineering. This paper summarizes the most significant research work published on the durability of FRP bars in the past two decades. A comprehensive review of the literature on the durability of FRP bars indicates a significant increase in the number of studies in this area in the last decade. The durability tests conducted by the authors and others on the latest generation of GFRP bars subjected to stresses higher than the design limits, combined with aggressive mediums at elevated temperatures, have concluded that the strength reduction factors adopted by current codes and guidelines are conservative. These factors were based on limited test results that were carried out on the early generations of the GFRP products, which have now substantially changed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| 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".