Bétons à haute performance à base de ciments composés contenant du laitier et de la fumée de silice
Bibliographic record
Abstract
For each tonne of cement used, the cement industry emits an average of 0.9 t of CO2, which contributes to the greenhouse effect. To satisfy the demands of the concrete industry for cementing materials, new environmental requirements, and the implementation of a sustainable development policy, the use of supplementary cementitious material as a replacement of part of the Portland cement has proven to be an interesting avenue that has not yet been fully explored. Granulated blast-furnace slag has been and is being used as a supplementary cementitious material in replacement of cement in many countries. In Canada, its proportion is usually limited to 20-25% of cement replacement owing to a significant decrease in early age compressive strength as well as a lower scaling resistance. In this study, we have tried to show that by reducing the water:cement ratio we can increase cement replacement by slag up to 50% without harming its short-term compressive strength and scaling resistance. The concretes that were prepared had a workability comparable to that of the reference concrete without slag, sufficient compressive strength to allow demoulding after 24 h, very low chloride ion permeability even at 28 d, as well as very good freeze-thaw and scaling resistance, as long as it is water-cured for a slightly longer period.Key words: high-performance concrete, blast-furnace slag, sustainable development, superplasticizers, workability, durability, silica fume.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".