Retrofitting of Box Section Concrete Beams to Resist Shear and Torsion Using Near-Surface-Mount (NSM) GFRP Stirrups
Bibliographic record
Abstract
There are a few kinds of research about box section Reinforced Concrete (RC) beams under the effect of combined shear and torsional stresses. In the present research, the concept of adding external strengthening, in the transverse direction, is adopted. The paper focuses on using Glass Fiber Reinforced Polymer GFRP ropes as near-surface mount stirrups. Nine box-section concrete specimens were decanted and tested. Three variables are adopted, (1) GFRP stirrups diameter, (2) GFRP stirrups inclination, and (3) GFRP stirrups spacing. The experimental results showed considerable enhancement in the shear capacity of the strengthened beams by 8 to 56% depending on the effectiveness of the studied parameters. Ductility and toughness were improved when more intensive strengthening schemes were adopted. The shear capacity of all tested beams based on Egyptian code ECP 208–2019 is calculated and compared with that from American code ACI 440–2019 and Canadian code CSA-A23.3-04. The shear capacity from both Egyptian and American codes compared with experimental loads. The analytical results are conservative in some cases and unconservative in others while the analytical results in general are conservative. The Canadian code CSA-A23.3-04 is unconservative compared to the experimental results for the range of the studied parameters and specimens.
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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) | 0.002 | 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".