Nano-modified concrete at sub-zero temperatures: experimental and statistical modelling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it