Laboratory Study of the Effects of the Mixer Type and Mixing Time on the Volumetric Properties and Performance of a HMA with 30 Percent Reclaimed Asphalt Pavement
Bibliographic record
Abstract
This study examined the effects of the laboratory mixer type and mixing time on a hot-mix asphalt (HMA) using three different types of mixers and four different mixing times. The asphalt mix used is a semi-open graded mix (ESG-10) with 30% reclaimed asphalt pavement (RAP), and a range of tests were conducted including bitumen extraction by ignition, particle size distribution, maximum specific gravity (Gmm), a SUPERPAVE gyratory compactor (SGC), bulk specific gravity (Gmb), indirect tensile stiffness modulus (ITSM), and indirect tensile strength (IDT). The statistical analysis of variance (ANOVA) was also applied to quantify the effect of mixer type and mixing time. The results indicated that both mixing type and time had a significant effect on the properties of the HMA (volumetric properties and compactability) and that the type of mixer used also affected the performance of the HMA (stiffness and cracking resistance), with some mixers producing asphalt mixes with better properties than others. The study ultimately demonstrated that it is possible to produce a mix that exhibits good performance and meets or does not meet the compactability specifications depending on the mixer type used.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".