Potential for using recycled glass sand in engineered cementitious composites
Bibliographic record
Abstract
This paper outlines attempts to characterise a green engineered cementitious composite (ECC) with a matrix containing waste recycled glass sand (GS) as a replacement for the silica sand (SS) commonly used in ECCs. To assess self-healing rate in GS-ECCs, specimens were pre-cracked up to 60% of their original flexure deformations and left to heal under moist curing. Alkali–silica reaction expansion, compressive and flexural strength, mid-span beam deflection capacity, crack development, rapid chloride penetration and resistivity were tested to assess the performance of different sound and preloaded ECC specimens. In addition, results of scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy of healed cracks were evaluated. Mechanical and physical results of GS-ECCs showed performances that were better than or comparable to the corresponding SS-ECC. This study also reveals an acceleration and improvement in self-healing rate with GS replacement level. A C–(N,A)–S–H (calcium-(sodium, aluminium)-silicate-hydrate) with low calcium/silicon ratio was confirmed to be the main outcome in the self-healing products of GS-ECCs.
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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) | 0.000 | 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".