Effect of nano-calcium carbonate on early-age properties of ultra-high-performance concrete
Bibliographic record
Abstract
In this study, the effects of nano-calcium carbonate (CaCO 3 ) addition on the early-age properties of ultra-high-performance concrete cured at simulated cold and normal field conditions were investigated. The nano-CaCO 3 was added at rates of 0, 2·5, 5, 10 and 15% as a partial volume replacement for cement. Similar mixtures incorporating chloride- and non-chloride-based accelerating admixtures were also tested for comparison. Results indicate the high potential of nano-CaCO 3 to accelerate the setting and hardening process of ultra-high-performance concrete through providing nucleation sites, increasing contact points and increasing the effective water-to-cement ratio. Although nano-CaCO 3 exhibited a comparable or slightly lower accelerating effect to that of the chloride- and non-chloride-based accelerating admixtures, it brings a number of benefits to concrete production. These include the development of low-maintenance structures through eliminating the risk of steel corrosion induced by chloride-based accelerating admixtures, as well as a more environmentally friendly concrete through reducing the cement factor of ultra-high-performance 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 | 0.000 |
| 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".