Ground Recycled Glass to Improve the Compressive Strength of Concrete
Bibliographic record
Abstract
This paper explores the use of recycled ground glass as a partial substitute for sand in concrete mixtures, aiming to enhance its mechanical properties, specifically compressive and flexural strength.Mixtures with glass replacements of 15%, 20%, and 25% were developed and tested, evaluating their performance at 7, 14, and 28 days.Experimental results indicate that using 15% ground glass increases compressive strength by 4.91% compared to standard concrete and improves workability without significantly affecting cohesion.In contrast, higher glass percentages increase the mixture's porosity, reducing its density and compressive strength.Flexural strength tests revealed that the 15% replacement achieved the best performance, with an increase of 2% compared to the control mix, while the 20% and 25% substitutions showed slight reductions in flexural capacity due to increased brittleness.These findings suggest that a moderate incorporation of ground glass not only enhances compressive behavior but also slightly improves flexural performance, making it suitable for structural applications with sustainability criteria.Additionally, an economic analysis was performed, showing that the use of recycled ground glass as a partial substitute for sand can reduce material costs in optimal replacement proportions, reinforcing the viability of this approach for practical applications.The research concludes that incorporating ground glass is a sustainable and economically feasible alternative, as it promotes waste reuse, reduces the demand for natural sand, and improves the overall mechanical performance of concrete.
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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 | 0.001 |
| 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".