Asphalt Material Characterization in Support of Mechanistic–Empirical Pavement Design Guide Implementation in Virginia
Bibliographic record
Abstract
The procedure proposed in the Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures (referred to as MEPDG) heavily depends on the characterization of the fundamental engineering properties of paving materials. This paper presents the results of a project aimed at the characterization of hot-mix asphalt (HMA) in accordance with the procedure established by MEPDG to support its implementation in Virginia. The project examined the dynamic modulus, the main HMA material property required by MEPDG, as well as creep compliance and tensile strength, which are needed to predict thermal cracking. Loose samples of 11 mixes (four base, four intermediate, and three surface mixes) produced with PG 64-22 binder were collected from different plants across Virginia. Representative samples underwent testing for maximum theoretical specific gravity, asphalt content by the ignition oven method, and gradation of the reclaimed aggregate. Specimens for the various tests were then prepared by use of the Superpave ® gyratory compactor. The test results showed that the dynamic modulus is sensitive to the mix constituent properties (aggregate type, asphalt content, percentage of recycled asphalt pavement, etc.) and that even mixes of the same type (SM-9.5A, IM-19.0A, and BM-25.0) had different measured dynamic modulus values. The Level 2 dynamic modulus prediction equation reasonably estimated the dynamic modulus measured; however, it did not capture some of the differences between the mixes found in the measured data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".