Use of FRP for RC Frames in Seismic Zones: Part II. Performance of Steel-Free GFRP-Reinforced Beam-Column Joints
Bibliographic record
Abstract
The use of FRP as reinforcement in concrete structures has been growing rapidly due to its advantages over conventional steel reinforcement (e.g., corrosion resistance, light weight, magnetic neutrality). A potential application of FRP reinforcement is in structural concrete frames. However, current seismic design standards and detailing criteria for beam-column joints were established for steel reinforcement and may be 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. Since there are no detailed specifications for the application of FRP reinforcement in seismic zones, research is needed to gain a better understanding of the behaviour of FRP-reinforced concrete under seismic loading. In this study, two full-scale beam-column joint specimens reinforced with steel and GFRP, respectively, were tested in order to investigate their performance in the event of an earthquake. The control steel-reinforced specimen is detailed according to the Canadian Code (CSA A23.3-94) recommendations. The GFRP-reinforced specimen is detailed in a similar scheme but using a GFRP grid. The behaviour of the two specimens under reversed cyclic loading, their load-storey drift envelope relationship and energy dissipation ability were compared. The GFRP-reinforced specimen showed a predominantly elastic behaviour up to failure. While its energy dissipation was low, its performance was acceptable in terms of total storey drift demand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".