Experimental Investigation of the Flexural Performance of Hybrid (GFRP and STEEL) Reinforced Concrete Beams
Bibliographic record
Abstract
The corrosion of steel reinforcement is a key contributor to the inability of concrete buildings to withstand long-term exposure to the elements. Fiber-reinforced polymer (FRP) has become an alternative to steel reinforcement in concrete structures to solve this problem. One drawback is GFRP's low modulus of elasticity is one drawback. The main objective of this study is to develop a practical hybrid FRP bar for concrete structures, especially for marine and harbor concrete structures, using the concept of material hybridization. The purpose of hybridization is to increase the elastic modulus of GFRP bars with acceptable tensile strength and improve their flexural ductility, as well as enhance the corrosion protection performance of both GFRP. GFRP crust with steel core bar is being considered in the development of hybrid FRP bars. The elastic modulus of the hybrid GFRP bar can be increased by up to 280 percent, according to the findings of this study. The bars are manufactured locally, utilizing a double-part die mold and locally sourced raw materials. Four-line static loading is used to examine the flexural limit states of nine beams with parameters of 200 mm width x 350 mm thickness x 2000 mm length, including pre-cracking behavior, cracking pattern, deflection, load capacity and strain, and failure mechanism.
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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 | 0.001 | 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".