Use of Nanoparticles in Cold Weather Masonry Construction
Bibliographic record
Abstract
Masonry systems are vulnerable to cold climates during construction and service. In North America, this issue is a major concern for masonry contractors as they either have to implement thorough heating practices for laying and curing masonry systems or postpone the construction to warmer seasons. This might lead to loss of productivity, delays in construction schedules, and, inevitably, extra costs. To minimize the adverse effects of cold weather on masonry construction, a new approach based on the use of nanoparticles in the mixture design of mortar joints is proposed. Previous research on the use of nanoparticles in cement-based materials has shown that nanoparticles can significantly accelerate the kinetics of hydration of cementitious binders under normal temperatures (22°C ± 2°C). In this study, an effort is made to assess the effect of nano-alumina (NA) and nano-silica (NS) with dosages of 0 %, 2 %, and 4 % by mass of masonry cement on the performance of mortar mixtures prepared and cured at 5°C. The key assessment criteria were based on fresh properties, compressive strength, and microstructural features. The results indicated that mortar mixtures containing NS had better performance than that of the other mixtures with or without NA, which suggests the promising use of NS in cold weather masonry construction.
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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) | 0.001 | 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".