Design of GFRP-reinforced rectangular concrete columns under eccentric axial loading
Bibliographic record
Abstract
The use of glass-fibre-reinforced polymer (GFRP) reinforcement as an alternative to steel for use in reinforced concrete (RC) structures has developed significantly in recent years. With excellent corrosion resistance, a high tensile strength to weight ratio and being non-magnetic and non-conductive, GFRP is an excellent solution for projects requiring improved corrosion resistance or reduced maintenance costs. However, despite a number of recent studies illustrating the effective use of GFRP rebars as longitudinal reinforcement for concrete compression members, the current international design codes do not recommend including GFRP reinforcement in the compression member capacity calculations. A test programme was thus carried involving the construction and testing of 17 rectangular concrete columns reinforced with steel or GFRP. This paper provides full derivations of the interaction diagrams for both steel- and GFRP-reinforced concrete columns. The interaction diagrams fitted the experimental data very well for both types of RC column. It was found that the GFRP-reinforced columns did not have a ‘balance point’ on the interaction diagram, and this was clearly shown for longitudinal reinforcement ratios above 3%. It was found that excluding the strength and stiffness of GFRP reinforcement from concrete compression calculations is conservative. Theoretical capacities better represent the experimental data when the strength and stiffness of GFRP reinforcement are included. The resulting factored interaction curves were exceeded by all experimental capacities.
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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 | 0.001 | 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".