Cold weather concreting: provisions of codes and research advances
Bibliographic record
Abstract
In cold regions, concrete practitioners face challenges in achieving the target performance criteria of concrete produced at low temperatures. When concrete temperature drops to −2.8°C, the hydration development of cementitious binders nominally ceases due to the freezing of mixing water, which results in hydraulic and osmotic pressures that exceed the tensile capacity of concrete, especially at early age (immature stage). Subsequently, the hardening and strength gain rates of concrete are adversely affected, resulting in insufficient microstructural development and irreversible deterioration, which makes concrete applications challenging under cold weather. Therefore, multiple investigations have been conducted to develop efficient approaches to overcome the challenges of placing concrete at low temperatures. The current paper synthesises code provisions in North America and Europe and state-of-the-art knowledge on cold weather concreting, in terms of mixture components as well as new inventions and methods of concrete curing and protection at low temperatures. Hence, it should provide informative guidance for the construction industry in cold regions to improve cold weather concreting practices.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".