Influence of Selected Mix Design Factors on the Thermal Behavior of Lightweight Aggregate Asphalt Mixes
Bibliographic record
Abstract
Abstract Lightweight aggregate (LWA) asphalt mix has been found to have lower thermal conductivity and thermal diffusivity compared to a conventional asphalt mix. Given these beneficial thermal properties, a LWA-asphalt mix can reduce frost penetration into the underlying pavement layers. It is therefore postulated that the LWA-asphalt mix can reduce damage associated with frost heave. This paper investigates the effects of binder grade and coarse aggregate content on the thermal properties of LWA-asphalt mixes. The effects of three asphalt binder grades (PG58-28, PG64-28, and PG70-28) and three aggregate gradations were investigated. The thermal conductivity was found to vary from 0.77 to 0.87 W/m°K for the LWA-asphalt mixes having bulk density in the range of 1585–1653 kg/m3 and air voids 3.5–4.5 %. Thermal diffusivity was determined to vary from 2.1 × 10−7 m2/s to 2.6 × 10−7 m2/s and a standard deviation of 0.18 × 10−7 m2/s. Specific heat capacity was found to be in the range of 1990 J/kg⋅°K to 2360 J/kg⋅°K, with a standard deviation of 130 J/kg⋅°K. Coefficients of variation for thermal conductivity, thermal diffusivity, and specific heat capacity were found to be 3.2, 6.1, and 7.6 %, respectively. These results show that the three thermal response variables vary in a very narrow range. It is concluded that the beneficial thermal properties of LWA-asphalt mixes appear to be insensitive to variation in asphalt binder grade and aggregate gradation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".