Anchorage System to Prestress FRP Laminates for Flexural Strengthening of Steel-Concrete Composite Girders
Bibliographic record
Abstract
Using externally bonded (EB) fiber-reinforced polymer (FRP) laminates for strengthening steel-concrete composite girders has recently received more attention from researchers. By prestressing the EB FRP laminates, the material is used more efficiently because a greater portion of its tensile capacity is employed and it contributes to the load-bearing capacity under both service and ultimate conditions. This is an ideal technique because it combines the advantage of using noncorrosive and lightweight advanced composite materials in the form of bonded FRP laminates with the high efficiency offered by external prestressing. An innovative mechanical anchorage system was developed to prestress the FRP laminates directly by jacking and reacting against the steel girder itself. The efficiency of the system was investigated using two types of FRP laminates for flexural strengthening of large-scale steel-concrete composite girders. The used FRP composite materials included carbon-fiber-reinforced polymer (CFRP) plate and steel-fiber-reinforced polymer (SFRP) sheets. The developed anchorage/prestressing system was easy to use/apply and proved to be a feasible and practical system for prestressing both CFRP plate and SFRP sheet. The prestressing levels in the FRP laminates were sufficiently maintained. The prestressing losses were insignificant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".