Development of FRC Materials with Recycled Glass Fibers Recovered from Industrial GFRP-Acrylic Waste
Bibliographic record
Abstract
Fiber-reinforced concrete (FRC) and engineered cementitious composite materials have demonstrated promising requisite in construction industry owing to its superior mechanical and durability properties. In this study, a sustainable approach was taken, i.e., to use industry waste as a reinforcement with improved interfacial bonding leading to enhanced mechanical performance of FRC. An efficient in situ recycling process allowed the authors to extract glass fibers from glass fiber-reinforced polymer acrylic waste. Concrete mixes with low fiber dosages including 0.1%, 0.2%, and 0.3% (by volume) of recycled as well as virgin glass fibers were prepared. The slump of concrete was maintained ∼150 mm by using high water-reducing admixture (HWRA). Notably, lower amount of HWRA was required for raw glass fibers vis-à-vis recycled ones due to its hydrophobic nature. Overall, FRC enclosing 0.3% recycled glass fiber demonstrated >20% enhancement in compressive, split tensile, and flexural strength as compared to control (after 28 days of curing), also supported by morphological analysis.
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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) | 0.001 | 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 | 0.001 | 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".