COMPARISON OF MARSHALL AND SUPERPAVE GYRATORY VOLUMETRIC PROPERTIES OF SASKATCHEWAN ASPHALT CONCRETE MIXES
Bibliographic record
Abstract
Saskatchewan Highways and Transportation is investigating adding higher percentages of fractured coarse aggregate to asphalt concrete pavements to improve rutting performance. Higher percentages of fractured coarse aggregate are more costly to use, as aggregate is obtained from increasingly scarce glacial gravel deposits in Saskatchewan. This research aimed to investigate the influence of coarse aggregate fracture on rutting performance of typical Saskatchewan dense-graded mixes. Three Marshall mixes were compacted with varying percentages of fractured coarse aggregate. The asphalt content and gradation of the mixes were held constant, as was the asphalt cement. 75-blow Marshall specimens and modified Superpave gyratory compacted (SGC) samples of the test mixes were manufactured. Analysis of the volumetric properties between types of samples showed a difference between 75-blow Marshall and SGC samples of the same test mix. Duncans pairwise comparison statistical analysis found that the 65% fracture mix and the 85% fracture mix were similar, but the 45% fracture mix was different across the Marshall specimens. The same analysis across the SGC samples found that the 85% fracture mix was different, where the 45 and 65% fracture mixes grouped together. Several rut performance predictors were investigated for these test mixes; however, the volumetric investigation of the SGC samples was the only analysis to show a benefit to having 85% fracture in asphalt mixes. Subsequent analysis of the SGC samples shows differences in compaction slopes and densities at initial and design gyration levels between test mixes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".