Performance of Structural Concrete Frames Reinforced with GFRP Grid
Bibliographic record
Abstract
The use of fibre-reinforced polymers (FRP) rebar in structural applications has been getting increasing attention due to the advantages it offers over conventional reinforcement (e.g. durability, light weight, magnetic neutrality). A possible application of FRP rebar reinforcement is in the area of multi-storey structural frames. However, current design standards and detailing criteria for beam-column joints were established in the 1970’s and may be considered unsuitable for FRP reinforcement due to its different mechanical properties. During recent earthquakes, many structural collapses were initiated or caused by beam-column joint failures. There are no comprehensive seismic standards for the application of FRP materials. Consequently, research is needed to gain a better understanding of the behaviour of FRP materials and their interaction with traditional materials in such application in order to implement their use on solid grounds. In this study, two full-scale quasi-static loading tests were performed on beam-column joint specimens. The first test was performed on a joint specimen reinforced with steel and its behaviour was compared to that of a second similar test performed on a GFRP-reinforced joint specimen. It is shown that GFRP-reinforced frames can have satisfactory drift capacity, but their energy dissipation capacity is limited.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".