Strength, Shrinkage and Early Age Characteristics of One-Part Alkali-Activated Binders with High-Calcium Industrial Wastes, Solid Reagents and Fibers
Bibliographic record
Abstract
Alkali-activated binders (AABs) are developed using a dry mixing method under ambient curing incorporating powder-form reagents/activators and industrial waste-based supplementary cementitious materials (SCMs) as precursors. The effects of binary and ternary combinations/proportions of SCMs, two types of powder-form reagents, fundamental chemical ratios (SiO2/Al2O3, Na2O/SiO2, CaO/SiO2, and Na2O/Al2O3), and incorporation of polyvinyl alcohol (PVA) fibers on fresh state and hardened characteristics of 16 AABs were investigated to assess their performance for finding suitable mix compositions. The mix composed of ternary SCM combination (25% fly-ash class C, 35% fly-ash class F, and 40% ground granulated blast furnace slag) with multi-component reagent combination (calcium hydroxide and sodium metasilicate = 1:2.5) was found to be the most optimum binder considering all properties with a 56 day compressive strength of 54 MPa. The addition of 2% v/v PVA fibers to binder compositions did not significantly impact the compressive strengths. However, it facilitated mitigating shrinkage/expansion strains through micro-confinement in both binary and ternary binders. This research bolsters the feasibility of producing ambient cured powder-based cement-free binders and fiber-reinforced, strain-hardening composites incorporating binary/ternary combinations of SCMs with desired fresh and hardened properties.
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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 | 0.001 | 0.001 |
| 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".