Repeatability of Workability Test Methods of Self-Consolidating Concrete
Bibliographic record
Abstract
In general, in the evaluation of a test method, the repeatability tests must be performed to establish upper and lower bounds for the precision of a test method. The repeatability is calculated as a standard deviation and relative error of test results. In order to evaluate the repeatability of the workability tests used in self-consolidating concrete (SCC) applications, an SCC mixture proportioned with 0.38 w/cm, Type MS cement, 480 kg/m3 of binder , and crushed aggregate with MSA of 12.5 mm was used. The dosage rate of the HRWRA of the SCC was adjusted to secure two initial slump flow consistency levels of 630 ± 10 mm and 700 ± 10 mm for the repeatability tests. For each consistency level, the concrete was batched five times (for a total of 10 mixtures). Each test was repeated five times by the same operator in order to establish the single-operator precision values. Furthermore, five different operators were used to perform each of the workability tests in order to assess the multi-operator error that could occur during testing. Based on the relative errors obtained in the repeatability tests, relatively low error values were obtained for the slump flow, J-Ring flow, and L-box blocking ratio tests. Relative errors for recommended SCC workability test methods were also summarized.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".