Softened Membrane Torsional Model for GFRP–Reinforced Concrete Bridge Box Girders
Bibliographic record
Abstract
This study investigates the experimental and analytical torsional behavior of reinforced concrete (RC) bridge box girders reinforced with glass fiber–reinforced polymer (GFRP) bars and continuous spiral stirrups, representing a first in literature. Reinforced concrete box girders were constructed and examined until failure to assess the influence of the spiral pitch and web reinforcement configuration on torsional behavior and strength. The test specimens had continuous GFRP spirals and tie stirrups; the control specimen did not have web reinforcement. The specimens were 4,000 mm long, 380 mm wide, and 380 mm high, and had a wall thickness of 100 mm. The test results demonstrate that the box girder with spiral GFRP reinforcement achieved higher torsional strength and lower twist than its counterpart specimen reinforced with individual GFRP tie stirrups by approximately 6% and 11%, respectively. The specimen with a narrow spiral pitch performed better than the specimens with a wide spiral pitch. An analytical iterative softened membrane model for torsion (SMMT) was used to estimate the entire torsional behavior of box girders with spiral GFRP reinforcement. The analytical results were compared with the experimental results of four bridge box girders with spiral GFRP reinforcement to validate the model's accuracy. The comparison indicates that the model could reasonably predict the cracking and ultimate torsional strength as well as the associated twists.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".