Properties of Nanosilica-Modified Concrete Cast and Cured under Cyclic Freezing/Low Temperatures
Bibliographic record
Abstract
Abstract Cold weather concreting is one of the most challenging problems facing concrete placement in many regions. For example, in Canada, low temperatures limit the construction season to a few months, usually between May and September. The incorporation of nanosilica in concrete, which has vigorous reactivity because of its ultrafine surface area, may enhance the hydration process and properties of concrete cast at low temperatures; however, this has not been substantiated. Therefore, this study focused on developing nanomodified concrete mixtures that were mixed, placed, and cured at cyclic temperatures (−5°C and 5°C), targeting applications in early fall and late spring periods in North America. The study followed the design of experiments modeling approach to test 15 concrete mixtures based on the response surface method. Three parameters were considered in the model: incorporation of fly ash (up to 25 %) and nanosilica (up to 4 %) as well as a combination of two types of antifreeze admixtures (calcium nitrate and nitrite). The mixtures were assessed based on setting time (placement), 3- and 28-day compressive strengths (hardened properties) and absorption (infiltration of fluids). Moreover, mercury intrusion porosimetry, thermal analysis, and scanning electron microscopy were conducted to characterize the microstructural features. The results showed that nanosilica, even with the inclusion of fly ash, significantly enhanced the overall performance and development of the microstructure of concrete mixed, cast, and cured at cyclic freezing/low temperatures. Thus, nanomodified concrete has promising potential for extending the construction season during early fall and late spring periods in cold regions.
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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) | 0.002 | 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".