Properties of Geopolymer Concrete Made With Recycled Concrete Aggregates and Glass Fibers
Bibliographic record
Abstract
This research aims to evaluate the workability and compressive strength of geopolymer concrete made with recycled concrete aggregates (RCA) and glass fibers (GF).The ambient-cured geopolymer concrete was prepared using a binder blend of slag and fly ash (3:1) and fine aggregates in the form of desert dune sand.Natural aggregates or RCA served as the coarse aggregates, while sodium silicate and sodium hydroxide formulated the alkaline activator solution.The investigated process parameters included the replacement percentage of natural aggregates with RCA, binder content, amount of additional water (added to the alkaline activator solution), particle size distribution of RCA, and volume fraction of GF.Experimental results indicated that replacing natural aggregates with 100% RCA had an insignificant impact on the workability but reduced the compressive strength by up to 25%.Increasing the binder content from 300 to 450 kg/m 3 in RCA geopolymer concrete reinforced with up to 2% GF, by volume, led to up to 28, 106, and 17% higher workability and 1-and 7-day compressive strengths, respectively.Adding up to 50 kg/m 3 of water increased the workability up to 230 mm while decreasing the compressive strength by up to 22%.Meanwhile, sieving the RCA to remove particles smaller and larger than 4.75 mm and 19 mm, respectively, increased the slump to 210 mm and 1-and 7-day compressive strength by 29 and 35%, correspondingly.The addition of 1 and 2% GF, by volume, decreased the workability by 2 and 9%, respectively, in comparison to the plain RCA geopolymer concrete mix, while the compressive strength was unaffected.Experimental findings highlight the possibility of utilizing RCA as a replacement for natural aggregates in slag-fly ash blended geopolymer concrete reinforced with glass fibers without compromising performance.Such beneficial use of RCA serves as a means of alleviating the adverse environmental impact associated with its disposal.
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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 | 0.000 | 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".