Influence of viscosity modifier addition methods on the rheological behaviour of alkali-activated slag systems
Bibliographic record
Abstract
Alkali-activated materials (AAMs) are emerging as sustainable alternatives to traditional binders in the construction industry. While the compatibility of various admixtures, such as superplasticizers, water reducers, and retarders, has been studied in one-part AAMs, a significant gap exists in understanding the effects of viscosity-modifying admixtures (VMAs). This study investigates the compatibility and impact of incorporating VMAs into one-part alkali-activated slag (AAS) paste, as well as the associated environmental impacts of using VMAs.Two factors were considered: the effect of different addition of VMAs methods (adding VMA separately, undissolved in water, or dissolved in water) and timings (early addition, 1 min after dry mixing the powder ingredients, and delayed addition, 25 min after mixing and resting). Initial flow diameter, flow retention, setting time, rheological properties (yield stress and plastic viscosity), heat of hydration, compressive strength (at 3, 7, and 28 days), and drying shrinkage were evaluated. Findings showed that delayed and separate addition of VMA significantly prolonged setting times due to slower distribution within the paste, regardless of the water-to-binder (w/b) ratio. Increasing the w/b ratio diluted the paste and reduced the significance of the method and timing of VMA addition on all measured properties. This research provides critical insights into the strategic use of VMAs to enhance the control of the rheological performance of AAS systems, contributing to the advancement of sustainable construction materials. • Adding VMA separately or combined with mixing water will impact AAS rheological properties differently. • Separate addition of VMA than mixing water significantly prolonged setting times • The timing of VMA addition greatly influences the rheological properties of one-part AAS. • Adding VMA reduces the drying shrinkage of one-part AAS. • Minor influence of timing and adding method of VMA at high w/c mixtures.
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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 | 0.001 |
| 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 | 0.001 |
| 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".