Influence of Slag Aggregate Production on Its Potential for Use in Internal Curing
Bibliographic record
Abstract
Internal curing is effective at reducing shrinkage and early-age cracking in cementitious systems with low water-to-cementitious materials ratios. In the United States, internal curing is typically accomplished using prewetted lightweight aggregate made by expanding slate, clay, or shale. This research focused on the use of porous slag aggregate, a byproduct of the iron and steel industry, for the internal curing of concrete. Five aggregates were evaluated for use in internal curing. The aggregates were produced from different manufacturing processes. Expanded, pelletized, and air-cooled slag aggregates were chosen for advanced testing. The research began by measuring the absorption and desorption properties of the aggregates. Laboratory testing of concrete mixtures containing select aggregates was performed to evaluate mechanical and durability properties. Full-scale testing was carried out with concrete produced at a ready-mix plant. A conventional department of transportation bridge deck mixture was compared with a similar concrete that was internally cured with prewetted expanded slag aggregate. Internally cured concrete made with expanded slag aggregate was shown to reduce shrinkage cracking with similar or improved overall mechanical and durability properties when compared with the conventional mixture.
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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 | 0.001 | 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".