Reducing Set Retardation in High-Volume Fly Ash Mixtures with the Use of Limestone
Bibliographic record
Abstract
High-volume fly ash (HVFA) concretes are attractive not only because they reduce cement content and the associated greenhouse gases, but also because they avoid landfilling excessive quantities of fly ash. These sustainability benefits are often tempered by practical constructability limitations that may exist for HVFA concretes: retardation and diminution of the early-age reaction, delay in setting (and finishing operations), and lower early-age strength. This paper explores the alleviation of these deficiencies in HVFA mixtures by the incorporation of fine limestone powders into ternary blends. Isothermal calorimetry and Vicat needle penetration measurements are employed to assess reaction rates and setting times, respectively. A systematic variation of the content and fineness of the limestone powder in mixtures containing either a Class C or a Class F fly ash indicates that setting times are linearly correlated with the surface area supplied by the limestone. Comparison of a limestone system to a system containing an inert titanium dioxide of similar particle size indicates that the acceleration and amplification effects of the limestone can be attributed to both physical (nucleation) and chemical (additional calcium ions) processes. The results indicate that ternary blends with 40% of the cement by volume replaced by 30% to 35% fly ash and 5% to 10% limestone at a constant water volume fraction can be achieved without significant delay in setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".