Cold weather concrete: current practices and innovative mix designs and protection methods
Bibliographic record
Abstract
Concreting in cold weather can be challenging because low temperatures can slow down the hardening processes of the concrete. Reducing the heating requirements for cold weather concreting is an area where further research and development are needed. This study allows for a thorough exploration of the effects of multiple variables on the properties of concrete under cold weather conditions. In Phase I, two commercial winter concrete mixtures of Type 6 (supplier I (L) and supplier II (C)) were chosen for testing, and the curing process was conducted at four different temperatures: -5, 0, +5, and +23°C. The protection was done using a commercial tarp as per current field practices for pavements. According to the results, the C and L concrete mixtures exhibited better mechanical and durability properties at temperatures of 0 and +5°C, and the tarp was inadequate at -5°C temperature. In Phase II, the impact of two antifreeze additives: calcium nitrate-based (CN) and urea, on the performance of concrete containing nano-silica, was assessed. The concrete was cast and cured at -5°C. The concrete specimens modified with nano-silica and containing CN exhibited the highest mechanical strength and durability properties, but the counterparts prepared with urea also exhibited satisfactory performance. In Phase III, internal curing of concrete was achieved by Lightweight Aggregates (LWA): expanded shale (ES) and slag-based (SB) aggregates, that were saturated with phase change material (PCM). The test variables included the use of different LWA contents (15% and 30% as a replacement for normal aggregate) and sizes (fine and coarse aggregates). The mixtures were prepared, cast, and cured at -15°C. The properties of concrete were directly affected by the size, type, and amount of LWA. The mixtures with 30% ES exhibited superior mechanical and durability properties compared to the SB mixtures and mixtures with 15% replacement, respectively. The size of LWA led to a divergent trend between the ES and SB mixtures. While the mixtures containing expanded shale fine aggregate (ESFA) demonstrated superior performance compared to those containing expanded shale coarse aggregate (ESCA), the trend was reversed for SB mixtures, depending on the absorption capacity to PCM.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".