Influence of Waste Slurry as Mixing Water on the Properties of C80 Concrete with Different Mineral Admixtures
Bibliographic record
Abstract
This paper mainly explores how waste slurry as mixing water affects the properties of C80 concrete.The waste slurry was collected from a mixing station.Two types of C80 concrete were prepared with different mineral admixtures: type I concrete mixed from 15% fly ash and 20% slag powder, and type II concrete mixed from 20% fly ash and 10% silica fume.The properties of the two types of concrete were evaluated in terms of working performance, mechanical properties, and durability.In addition, the influence of waste slurry on microstructure of concrete was analyzed through X-ray diffraction (XRD).The results show that, with the growing content of waste slurry, slump and expansion were declining; the initial and final setting times gradually increased, but the increments were not significant; with the growing content of waste slurry, the 7d compressive strength of type I concrete stayed below that of reference concrete, and gradually decreased, but the later compressive strengths increased rapidly; the 7d compressive strength of type II mineral admixture concrete gradually increased, while the later compressive strengths increased first and then decreased.Besides, the addition of waste slurry enhanced the resistance to chloride ion penetration (CIP), and increased the carbonization depth of concrete.The 7d XRD peak of using tap water as mixing water was slightly higher than that of using waste slurry as mixing water; the 28d XRD peak of the former was slightly lower than that of the latter.The research provides reference for applying waste slurry in concrete production.
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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 | 0.001 |
| 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".