Roller-Compacted Concrete Mix Design Procedure with Gyratory Compactor
Bibliographic record
Abstract
Roller-compacted concrete (RCC) pavements are durable and can be built rapidly and economically. In previous research, the gyratory compactor has proved to be a reliable instrument and overcome the drawbacks of other methods used to mold laboratory and field samples. Work is under way by different organizations in Canada, Japan, and the United States to improve mix design procedures for RCC. One procedure widely used for RCC pavements was developed by the U.S. Army Corps of Engineers. This research investigated improving mixture proportioning of RCC by using a gyratory compactor. Previous research suggested that the gyratory compactor could be used effectively to produce RCC test samples for both compressive and splitting tensile strength. An RCC mix design procedure using the gyratory compactor can benefit many transportation agencies because it provides a reliable way to evaluate the strength and density of RCC mix designs. Results for several RCC mixtures compacted to different densities are presented, along with comparisons with modified Proctor test results. Using the gyratory compactor to design better RCC mixtures might lower the costs and improve the reliability of RCC pavements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".