Prediction on Autogenous Shrinkage of Self-Consolidating Concrete
Bibliographic record
Abstract
Proper estimate of autogenous shrinkage of self-consolidating concrete (SCC) can provide engineers with the information necessary for producing high quality products manufactured with SCC. An experimental program was undertaken to evaluate autogenous shrinkage of precast, prestressed SCC. Sixteen SCC with slump flow of 680 ± 20 mm were evaluated. These mixtures were made with 440 to 500 kg/m3 of binder, Type MS cement or HE cement and 20% Class F fly ash, 0.34 to 0.40 w/cm, viscosity-modifying admixture content of 0 to 100 mL/100 kg of binder, and 0.46 to 0.54 sand-to-total aggregate volume ratio. Two high-performance concretes (HPC) with 0.34 and 0.38 w/cm and slump of 150 mm were also investigated. HPC developed similar autogenous shrinkage at 56 days compared to SCC made of a given binder type. Shrinkage was compared to prediction models proposed by Tawaza and Miyazawa 1997, Jonasson and Hedlund 2000, and CEB-FIP 1999. The Tazawa and Miyazawa model was modified to provide adequate prediction of autogenous shrinkage for precast, prestressed SCC.
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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 | 0.000 | 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) | 0.000 | 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".