Interaction between cement clinker constituents and clay minerals and their influence on the strength of cement-based stabilized soft clay
Bibliographic record
Abstract
The strength of cement-based stabilized clay is less than that of concrete and mortar and shows a distinct variability owing to the existence of various clay minerals. To better understand the cement–clay reactions and the strength generation, two artificial clays with the unique clay mineral and major strength-producing constituents of cement clinker were investigated via mechanical, compositional, and microstructural analyses. Results show that C3A-stabilized clay gains strength rapidly in the first 3 days, but this favourable tendency vanishes over time. After 90 days of curing, the strength of C3S-stabilized clay is about four times that of the corresponding C3A-stabilized clay, indicating the remarkable long-term stabilization efficiency of C3S. Furthermore, clay minerals primarily draw into strength evolution in the reaction process. Despite that bentonite is more reactive than kaolin as long as the highly alkaline conditions persist, it has a higher probability to flocculate into large aggregates during the mixing process, which may impair the reaction efficiency and even brings adverse stabilization effects, suggesting the importance of uniformity control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".