Retrofitting Actual-Size Precracked Precast Prestressed Concrete Double-Tee Girders Using Externally Bonded CFRP Sheets
Bibliographic record
Abstract
Precast prestressed double-tee (DT) girders are considered a crucial element of modern infrastructure used to accelerate building construction worldwide. These girders can be deficient because of local damages and develop cracks as a result of improper transportation and handling. Therefore, it is very important to develop an efficient retrofitting technique in order to restore the lost capacity and/or even outperform it. The objective of this paper is to develop retrofit strategy of the deficient girders using the externally bonded carbon-fiber-reinforced polymer (EB-CFRP) technique and verify it using field data. A field test was conducted on three actual-size precast pretensioned DT girders having different levels of damage and retrofitted using CFFP sheets. The girder stems for two of them were strengthened in flexure using unidirectional U-shaped CFRP sheets, while all girders were shear-strengthened at their dapped ends. Each girder was loaded incrementally up to collapse while the deflection of the girder and normal strains developed on both concrete surface and the CFRP sheets were recorded at each load increment. Test results assured the adequacy of the adopted strengthening technique with respect to both ultimate capacity and ductility. In addition, the experimental flexural and shear resistances of the retrofitted girders are far greater than those obtained from equations available in design codes by at least 60%.
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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) | 0.001 | 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 | 0.001 | 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".