Nano-modified concrete at sub-zero temperatures: experimental and statistical modelling
Bibliographic record
Abstract
In cold regions, concrete practitioners face challenges when trying to achieve quality results with concrete produced under low temperatures. The addition of nano-silica, which has vigorous reactivity, to concrete can produce mixtures with a dense microstructure and improved hardened properties under cold temperatures. Thus, this research focused on gaining a fundamental understanding of the performance of nano-modified concrete which was mixed, cast and cured at a temperature of −5°C, without any method of heating or insulation. This study adopted the response surface method as a statistical modelling approach to assess the effect of different parameters on the performance of 28 mixtures. Four factors were implemented in this model – water/binder ratio, fly ash content (0–25%), nano-silica dosage (0–4%) and type of antifreeze admixtures – followed by optimisation scenarios. The mixtures’ performance was assessed based on multiple responses: initial and final setting times, early- and late-age compressive strengths and resistance to freezing–thawing cycles. In addition, mercury intrusion porosimetry, thermogravimetry and backscattered scanning electron microscopy were conducted to capture the microstructural evolution of the mixtures. Nano-modified mixtures with and without fly ash, especially with a low water/binder ratio (0·32) and high calcium nitrite content, showed promising performance when cast under cold weather conditions without any protection method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".