Statistical Models to Predict Fresh Properties of Self-Consolidating Concrete
Bibliographic record
Abstract
In order to understand the influence of mixture parameters on concrete behaviour, a factorial design was employed in this investigation to identify the relative significance of primary mixture parameters and their coupled effects (interactions) on fresh properties of SCC that are of special interest to precast, prestressed applications. In addition to the 16 SCC mixtures employed, three SCC mixtures corresponding to the central point of the factorial design were prepared to estimate the degree of the experimental error for each of the modeled responses. The mixtures were evaluated to determine several key responses that affect the fresh properties of precast, prestressed concrete, including filling ability, passing ability, filling capacity, surface settlement, and column segregation. Mixture parameters modeled in this investigation included the binder content, binder type, w/cm, sand-to-total aggregate ratio (S/A), and dosage of thickening-type, viscosity-modifying admixture (VMA). The factorial design can identify potential mixtures with a given set of performance criteria that can be tried in the laboratory, hence simplifying the test protocol needed to optimize 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".