Investigating the Workability and Mechanical Properties of Fly Ash-Glass Waste Geopolymer Concrete with Recycled Steel Can Fibers
Bibliographic record
Abstract
Concrete plays a vital role in civil engineering and infrastructure development.As the focus shifts toward sustainable practices, innovative approaches such as geopolymer concrete and the use of glass waste as aggregates have emerged, improving both the environmental impact of concrete and its mechanical properties.Fiber reinforcement, especially with recycled materials, has gained attention for enhancing the sustainability of concrete, with commercial storage materials such as steel cans becoming viable reinforcement.This study examined the workability, compressive strength, and tensile strength of fly ash-glass waste fiber-reinforced geopolymer concrete (FRGC) using recycled steel can fibers.Samples were produced by crushing soda-lime glass bottles and cutting steel cans into hook-end fibers, with an M20 concrete mix formulated by substituting 30% of cement with fly ash and 30% of coarse aggregate with glass waste.Recycled steel can fibers were added at varying percentages (0-5% by weight of cement).Results showed a decrease in workability as recycled fibers were added.Although compressive strength also decreased, the reduction was insignificant at 4% fiber content.The addition of fibers improved tensile performance, though the increase remained statistically insignificant compared to the control group.Notably, concrete samples containing recycled steel can fibers exhibited ductile failure and fiber bridging.Overall, the fly ashglass waste FRGC with 4% recycled steel can fibers demonstrated favorable outcomes in compressive and tensile strengths.This study highlights the potential of using recycled steel cans to enhance concrete sustainability.
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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 | 0.000 | 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".