Shear Strengthening of RC Beams Using Sprayed Glass Fiber Reinforced Polymer
Bibliographic record
Abstract
The effectiveness of externally bonded sprayed glass fiber reinforced polymer (Sprayed GFRP) in shear strengthening of RC beams under quasi-static loading is investigated. Different techniques were utilized to enhance the bond between concrete and Sprayed GFRP, involving the use of through bolts and nuts paired with concrete surface preparation through sandblasting and through the use of a pneumatic chisel prior to Sprayed GFRP application. It was found that roughening the concrete surface using a pneumatic chisel and using through bolts and nuts were the most effective techniques. Also, Sprayed GFRP applied on 3 sides (U-shaped) was found to be more effective than 2-sided Sprayed GFRP in shear strengthening. Sprayed GFRP increased the shear load-carrying capacity and energy absorption capacities of RC beams. It was found that the load-carrying capacity of strengthened RC beams was related to an effective strain of applied Sprayed GFRP. This strain was related to Sprayed GFRP configuration and the technique used to enhance the concrete-FRP bond. Finally, an equation was proposed to calculate the contribution of Sprayed GFRP in the shear strength of an RC 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 | 0.000 | 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) | 0.001 | 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".