Effects of Aluminum Sulfate and Quicklime/Fluorgypsum Ratio on the Properties of Calcium Sulfoaluminate (CSA) Cement-Based Double Liquid Grouting Materials
Bibliographic record
Abstract
Grouting materials are used frequently in grouting reinforcement projects, such as mining and coastal engineering. Double liquid grouting materials are mostly used because of the fast setting and high early strength properties when the two slurries are mixed together but high fluidity when the two slurries are separated. In our study, double liquid grouting materials were developed from CSA cement (slurry A), quicklime and fluorgypsum (slurry B). Aluminum sulfate was added in slurry B in order to counteract any adverse effects caused by the fluorgypsum, such as the decreased early compressive strength and the prolonged setting time. The effects of aluminum sulfate content and the quicklime/fluorgypsum ratio on the setting time, hydration heat, and compressive strength of the double liquid grouting materials were investigated, and the hydration products were characterized through thermogravimetry-differential thermal analysis (TG-DTA), X-ray Diffraction (XRD), and Scanning Electron Microscope (SEM) tests. The results show that the addition of aluminum sulfate can shorten the setting time and increase compressive strength at both early and later ages. Considering the setting time and compressive strength of double liquid grouting material at the same time, the optimum content of aluminum sulfate was found to be 2%, and the optimum ratio of quicklime/fluorgypsum was found to be 2:8. The values of the optimum content of aluminum sulfate and ratio of quicklime/fluorgypsum were verified from theoretical analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".