Effect of aggregate type, gradation, and compaction level on thermal properties of hot-mix asphalts
Bibliographic record
Abstract
This paper investigates the effects of mix design factors on the thermal properties of Superpave asphalt concrete. The thermal properties were measured using a guarded testing device (k-alpha device) developed recently at the University of New Brunswick. Two aggregate types, three gradations (determined by the material retained on a 4.75 mm sieve), and four compaction levels (at 35, 70, 100, and 160 gyrations) were investigated. The aggregate was 12.5 mm maximum nominal size with PG58-34 asphalt binder used for all mixes. The findings show that the thermal conductivity of the asphalt concrete ranged from 1.7 to 2.1 W/m·K. The specific heat capacity varied with the aggregate source and ranged from 940 to 2000 J/kg·K. The thermal diffusivity was found to vary with aggregate source. The aggregate type was found to have the most significant effect on the thermal properties. The compaction level had an effect but not a statistically significant one. The range of thermal properties determined in this paper is comparable to that found in the literature.Key words: thermal properties, asphalt concrete, pavement temperature prediction, heat conduction, transient state, thermal conductivity, thermal diffusivity, heat capacity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".