Fatigue Behavior of Segmental Precast Concrete Decks Post-Tensioned with GFRP Rods
Bibliographic record
Abstract
This study investigates the cyclic behavior of segmental precast concrete decks reinforced and post-tensioned with glass fiber–reinforced polymer (GFRP) rods, replicating the repetitive loading conditions induced by sea waves during service. Large-scale decks with different levels of post-tensioning in the GFRP rod and loading conditions were prepared and evaluated under a repetitive three-point flexural load. The results of the experimental tests, including load–displacement behavior, strain response, axial load, and failure mechanisms, are presented and analyzed. According to the results, the level of fatigue loading strongly impacts the behavior of GFRP-RC monolithic concrete decks. It was shown that a monolithic deck with a reinforcement ratio of 1.68% could withstand 1 million cycles of wave loading at fatigue stress equal to 0.21 Mu (ultimate bending moment) without showing any sign of cracking. However, increasing the fatigue stress to 0.3 Mu caused failure after 20,585 cycles due to shear compression failure. By applying a precompression stress of 1 MPa, the segmental precast concrete decks could achieve >1,000,000 fatigue cycles without any failure, but only a slight GFRP rod axial load relaxation. The outcome of this study enhances the understanding of the fatigue behavior of the segmental deck with noncorrosive GFRP rods, demonstrating their suitability for onshore maritime infrastructures.
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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".