COMPARISON OF LABORATORY PRACTICES FOR ROLLER COMPACTED CONCRETEPAVEMENTS
Bibliographic record
Abstract
Many advantages like time, cost and sustainability provided by the use of Roller Compacted Concrete (RCC) are behind the increase of this use in pavements, dams, airports, industrial storehouse, military fields and other applications day by day. However, even that many countries started to use this technology since the 1970’s, especially Canada and USA, there is no fully efficient laboratory test method that can represent the compaction conditions of the field. This study aims to compare the main compaction methods that are used to prepare RCC specimens in the laboratory. To achieve this purpose, different RCC mixture designs were prepared by changing the three main factors that most affect the RCC concrete matrix through the use of two different cement dosages (200 and 400 kg/m3 ), two maximum aggregate sizes (Dmax 12 mm and 19 mm) and five different water contents (3-4-5-6-7%). Along with this, different compaction techniques (modified proctor, vibrating table, vibrating hammer and superpave gyratory compacter) were applied for each mixture. Then, the effects of RCC mixing parameters on the Vebe time, the density and the compressive strength were investigated. Especially for compressive strength, the use of a vibrating hammer gives higher density and strength values for lower cement content mixtures whereas other compaction methods exhibit lower values for all mixtures when compared to gyratory compactor.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".