Torsional behavior of GFRP-RC pontoon decks with an edge cutout and diagonal reinforcements
Bibliographic record
Abstract
Large-scale pontoon decks with edge cutouts were tested and the effect of diagonal reinforcements, arrangement of bars, and reinforcement-grid space size was investigated. The generated experimental data demonstrated the enhancement in the pre-cracking torsional behavior provided by diagonal reinforcement in double-layered reinforcement. Meanwhile, a further improvement in the reinforcement design with a denser grid spacing of 100 mm in double-layer mesh exhibited 137 % higher post-cracking torsional rigidity than that of the deck with a 250 mm grid spacing reinforcement. The former deck presented similar torsional behavior as the double-layered reinforced solid deck. The cracking torque and post-cracking torsional behavior of the pontoon decks can be accurately predicted with the introduced equations with consideration of the effective width related to the contribution of diagonal bars and considering the contribution of longitudinal GFRP bars, respectively.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".