The effect of alkalis from supplementary cementing materials on expansion due to alkali-carbonate reaction
Bibliographic record
Abstract
While much research has been completed on Alkali-Aggregate Reaction (AAR) over the years, few studies have focused on the lesser known Alkali-Carbonate Reaction (ACR). With the increasing use of supplementary cementing materials (SCM), it is important to determine the effects of SCM on ACR. In this research, the concrete prism test was used to evaluate the reactivity of Pittsburgh aggregate when used with cement and a combination of one or two types of SCM. Several concrete prism mixes were completed using eight (8) different types of SCM including slag, silica fume, metakaolin, and five different types of fly ash. The results from the concrete prism test showed that all of the mixes tested were not effective in reducing the expansion due to ACR to levels below the CSA expansion limit (0.04% at 2 years). However, it was found SCM did help reduce expansion due to ACR on a marginally reactive aggregate. The Concrete Microbar test was completed to evaluate the test's validity on determining ACR aggregates with and without SCM. The results from this experimental program suggest that the microbar test should not be used for the acceptability of an aggregate. SCM with low-alkali contents such as metakaolin and CI-LA fly ash were found to help reduce the detrimental expansion of concrete due to ACR.
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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 | 0.001 |
| 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) | 0.002 | 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".