A Comparative Study on the Efficiency of CFRP and GFRP in the Improvement of Compressive Strength, Acoustic Impedance and Bracing of Filled and Hollow Concrete Columns in Different Layers and Ages
Bibliographic record
Abstract
FRP technique is growing in popularity as a modern strengthening method. When it comes to FRP, concrete surface strength plays a determining role in the bond between FRP and concrete. This paper aims to compare the efficiency of CFRP and GFRP in the improvement of compressive strength, acoustic impedance and bracing of filled and hollow concrete columns in different layers and ages. In doing so, we carried out various tests on 18 samples in the ages of 3, 7, 14, 28, 42 and 90 days. According to the results, the strength of un-braced carbon and glass increased by 19-40% and 8-43% respectively and the strength of braced carbon and glass increased by 17-25% and 10-82% respectively. The compressive strength increased by 66% in one-layer CFRP hollow column, 96% in two-layer CFRP hollow column, 123% in three-layer CFRP hollow column, 36% in one-layer GFRP hollow column, 63% in two-layer GFRP hollow column, 105% in three-layer GFRP hollow column, 71% in one-layer CFRP filled column, 138% in two-layer CFRP filled column, 154% in three-layer CFRP filled column, 45% in one-layer GFRP filled column, 79% in two-layer GFRP filled column, and 144% in three-layer GFRP filled column. The ultimate strength of the beams with flexural-shear strengthening was higher than other beams. Also, the increased percentage of fiber resulted in the increased speed of ultrasonic waves.
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) | 0.000 | 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".