Seismic behavior of concrete bridge columns confined with fiber-reinforced polymer stay-in-place formwork
Bibliographic record
Abstract
One of the applications of fiber-reinforced polymers in bridge construction is stay-in-place formwork. Fiber-reinforced polymer stay-in-place formwork, in the form of preformed tubes, provides easy form assembly, protection of steel reinforcement and concrete against corrosion and chemical attacks while also improving the strength and ductility of structural elements in earthquake resistant construction. Experimental research was conducted to investigate the seismic performance of fiber-reinforced polymer stay-in-place formwork for bridge columns. Tests of large-scale specimens were conducted under simulated seismic loading. The experimental program included circular and square columns confined with carbon fiber–reinforced polymer tubes. The results indicate that the use of carbon fiber–reinforced polymer tubes increases column inelastic deformability significantly. Bridge columns under low levels of axial compression exhibit inelastic drift capacities in excess of 4% before failing in flexural tension due to the rupturing of longitudinal reinforcement. These observations and experimental results were used to develop a displacement-based design procedure for concrete confinement for fiber-reinforced polymer–encased concrete columns. This article presents an overview of the experimental program and the design approach developed.
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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 | 0.000 | 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".