Three Years of Dynamic Modulus Testing of Asphalt Mixes
Bibliographic record
Abstract
The dynamic modulus (E*) of an asphalt mix characterizes its stiffness response under sinusoidal loading. E* is a key input parameter into the Mechanistic-Empirical Pavement Design Guide (MEPDG) and is a required parameter for a Level 1 design. It is also a parameter with which most practitioners have little experience. In anticipation of the future implementation of the MEPDG, in 2005, Alberta Transportation established an annual E* testing program of select asphalt concrete pavement mixtures. The intent of this testing was to develop some background on the range of values that might be expected and to confirm the ability of E* to differentiate between different mix types. This paper documents the testing that Alberta Transportation has undertaken to date, including some trials with only duplicate samples, as well as some trials using four-inch thick samples. The results of the E* testing, the E* master curves, and a comparison of the results to those obtained through the Witczak predictive equation are also presented. (A)
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".