Performance of Hybrid Reinforced Concrete Beam Column Joint: A Critical Review
Bibliographic record
Abstract
Large residual strain in reinforced concrete structures after a seismic event is a major concern for structural safety and serviceability. Alternative reinforcement materials like fiber-reinforced polymer (FRP) have been widely used to mitigate corrosion problems associated with steel. Low modulus of elasticity and brittle behavior compared to steel has made the use of FRP unsuitable in seismic resistant strictures. A combination of steel-FRP reinforcement configuration can address the problem of corrosion. Therefore, introducing a material that shows strong post elastic behavior without any decay due to corrosion is in demand. Shape memory alloy (SMA), a novel material, is highly corrosion resistive and shows super elastic property. Coupling SMA with FRP or steel in the plastic hinge region allows the structure to undergo large deformations, but regains its original shape upon unloading. In this study, the performance characteristics of four previously tested beam-column joints reinforced with different configurations (steel, SMA/steel, glass fiber reinforced polymer (GFRP) and SMA/FRP) are compared to assess their capacity to endure extreme loading. Experimental results are scrutinized to compare the behavior of these specimens in terms of load-story drift and energy dissipation capacity. SMA/FRP and SMA/Steel couples have been found to be an acceptable approach to reduce residual deformation in beam-column joints with adequate energy dissipation capacity. However, SMA/FRP is superior to SMA/Steel concerning to the corrosion issue in steel.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".