Sample Preparation for Direct Tension Testing: Improving Determination of Asphalt Binder Failure Stress and Test Repeatability
Bibliographic record
Abstract
During Superpave direct tension testing (DTT) of asphalt binders, the failure stress and failure strain values were found to depend strongly on the molecules of asphalt in the DTT mold forming a stable network. An asphalt specimen in which molecules have formed a network with aligned dipole moments has a higher DTT failure stress value. A sample preparation method based on this theory was developed. In this method, ceramic tiles holding the DTT molds were heated in an oven with controlled temperatures specific to the performance grade (PG) of the binder. These heated ceramic tiles provided a means for the poured asphalt to stay fluid and cool slowly and uniformly. The theory, detailed method development, and sample preparation procedure are described. With this method, the DTT failure stress of asphalt binder is higher than in samples prepared without controlled cooling. The variability of failure stress and failure strain values of the six specimens within the same run is usually less than 15 percent for the softer asphalt, for example, PG52–34, and somewhat higher for the harder asphalt, for example, PG64–28. The average results of the best four out of six specimens usually agreed with another run within 10 percent, regardless of asphalt type. Important considerations during preparation of DTT samples are discussed. The incorporation of these details for the sample preparation method in the DTT procedure will lead to better agreement of direct tension results among different laboratories.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".