Confinement Characteristics of GFRP-RC Circular Columns under Simulated Earthquake Loading: A Numerical Study
Bibliographic record
Abstract
The seismic behavior of fiber-reinforced polymer-reinforced concrete (FRP-RC) columns is far from being fully explored. Therefore, numerical and analytical studies were performed to address the effects of different parameters and evaluate the current design provisions for confinement reinforcement under seismic loading. Using a commercially available software package, a three-dimensional nonlinear finite-element model (FEM) was constructed and validated against the experimental results of full-scale glass FRP (GFRP)-RC circular columns previously tested by the authors. The validated FEM was, then, used to conduct an extensive parametric study investigating the effect of concrete compressive strength, spiral pitch, axial load level, and column aspect ratio (i.e., shear span-to-depth ratio). It was found that increasing the concrete strength caused an increase in lateral load capacity and initial stiffness, whereas the drift capacity decreased. The latter property was also significantly affected by the variation of spiral pitch and axial load level. On the other hand, the aspect ratio had a marginal effect on moment or drift capacities. Using the results of the FEMs, two new design models were proposed. The proposed models showed remarkably better predictions than the equation adopted by the Canadian standard for the design of FRP-RC structures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".