Performance of FRP-Steel Joints and FRP-Steel Beams Fastened by FRP Anchors
Bibliographic record
Abstract
This paper reports on the experimental results of utilizing anti-corrosive FRP anchors in FRP-steel joints and FRP-steel beams for strengthening purposes.The FRP anchors were introduced in replacement of the steel bolts typically used in the mechanical fastening methods of strengthening steel structures.FPR laminates were fastened to steel plates in a double-lap arrangement using 10 mm and 13 mm diameter FRP anchors without a bonding agent.The tested joints showed good bearing between the anchors and the fastened FRP laminates.FRP-steel joints fastened by the 10 mm diameter anchors experienced early shear fracture, risking the joints' ductility.Better ultimate load capacity, bearing and ductility were attained by utilizing the 13 mm anchors compared to the 10 mm anchors.The fastening technique was then implemented on full-scale steel beams by fastening the FRP laminates to the beams' bottom flange using the 13 mm FRP anchors.The flexural performance of the beams under two point loads was reported in view of the loaddeflection curves, observed failure modes and strain measurements.The strengthened beams showed good bearing between the anchors and the laminates before the fracture of the anchors occurred.The strengthened beams showed higher ultimate load capacity than the unstrengthened beams, opening the horizon for additional investigations on the utilization of FRP anchors in strengthening steel beams.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".