Study on microstructural and mechanical properties of cementitious materials composed of fly ash and dacite powder
Bibliographic record
Abstract
The hydration reaction of mass concrete seriously endangers the structural safety. At present, the concrete production relies excessively on fly ash due to hydration reaction. In view of the problem that the demand of fly ash exceeds the supply, this paper proposes to use dacite powder to partially replace fly ash as the raw material for preparing cementitious materials. Through comprehensive tests and microstructure tests, various properties of dacite powder and fly ash composite cementitious materials are studied. The results show that: 1. The dacite powder with a specific surface area of 650 m2/kg, a fineness of 15% under laser particle size and a ball-milling time of 1.0 h has the best performance. 2. It is advisable to mix dacite powder and fly ash. The total amount of dacite powder should not exceed 30% of the cementitious material. It has the best performance when the amount of dacite powder and fly ash is the same. 3. The alkali activity of aggregate can hardly be inhibited by mixing dacite powder alone. The recommended measures to inhibit the alkali activity of aggregate are: i) mixing more than 20% fly ash alone. ii) mixed with 25% dacite powder and more than 15% fly ash.
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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 | 0.001 | 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".