Properties of nano-modified fly ash concrete cast and cured under cyclic low/freezing temperatures
Bibliographic record
Abstract
Under lower temperatures, the hydration process of cement slows down significantly, and it completely stops when the temperature goes below 0℃. This hinders strength development and durability of concrete. The current practices for overcoming these challenges involve methods such as heating concrete ingredients and surroundings to provide favorable curing conditions for concrete. However, these practices are associated with significant costs and adverse environmental effects due to the requirements of enclosure materials, highly-skilled manpower for quality control, and considerable consumption of energy and significant amounts of greenhouse gas emissions. Therefore, Phase I of this thesis focused on developing nano-modified concrete mixtures comprising cold weather admixture systems CWAS which were mixed, placed and cured at cyclic temperatures (-5 /5°C) targeting applications in early fall and late spring periods. This phase followed the design of experiments (DOEs) modeling approach to test 15 concrete mixtures. Three parameters were considered in the model: incorporation of fly ash (up to 25%) and nano-silica (up to 4%) as well as combination of two types of antifreeze admixtures (calcium nitrate and nitrite). The mixtures were assessed based on setting time (placement), compressive strengths (hardened properties) and absorption (infiltration of fluids). Moreover, microstructure analysis tests were conducted to characterize the microstructural features. The results showed that nano-silica, even with the inclusion of fly ash, significantly enhanced the overall performance and development of microstructure of concrete mixed, cast, and cured at cyclic freezing/low temperatures. Phase II of this thesis targeted developing durable repair mixtures. The experimental program in this phase was composed of setting time, compressive strength, fluid absorption, bond strength, surface scaling, restrained shrinkage as well as mercury intrusion porosimetry tests. Seven mixtures incorporation of fly ash (up to 25%) and nano-silica (up to 4%) as well as CWAS were selected to evaluate their potential use as cold weather repair materials for concrete infrastructure targeting late fall and early spring periods. The overall results of this phase showed that nano-modified concrete mixtures achieved a balance between early- and late-age properties and high compatibility with substrate concrete, thus a promising potential for their use as repair mixtures in cold regions.
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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) | 0.003 | 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".