Performance of Steel I-Beams Strengthened by Fastening Hybrid FRPStrips
Bibliographic record
Abstract
Fiber-Reinforced Polymers (FRPs) have been widely used in strengthening steel structures.Few researches were conducted to verify the adequacy of strengthening steel structures by fastening FRPs.The current paper reports on the outcomes of an experimental study aimed at strengthening steel I-beams by fastening hybrid FRP (HFRP) strips.A total of ten UB203x102x23 beams were tested in a four-point loading setup.Steel bolts were used to fasten the HFRP strips to the beam bottom flange.The effect of HFRP length was assessed by considering three scenarios (1,620, 1,170 and 810 mm) that correspond to 90%, 65% and 45% of the clear span length of tested beams.Single and double HFRP strips were utilized to evaluate the effect of HFRP thickness on the performance of the strengthened beams.Staggered and uniform bolt arrangements were used to assess the effect of bolt arrangement on the performance of the fastened systems.All strengthened beams exhibited better yield and ultimate strength as compared to the control beam.Fastening two HFRP strips with lengths of 90% of the beam clear span revealed 21% enhancement in the ultimate capacity of the strengthened beam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".