Quick Asphalt Binder Low-Temperature PG Determination Using DSR
Bibliographic record
Abstract
Thermal cracking at low temperature is a major asphalt pavement distress that can cause premature failure. For this reason, Superpave system has developed asphalt performance grade (PG) based on environment. Standard test method for determining low-temperature asphalt grade is AASHTO T 313 using BBR (bending beam rheometer) device. This test method has been well adopted; however, challenges exist with the test procedure for wider use. The BBR device needs constant calibration and the test is rather tedious and time consuming. The test involves use of hazardous liquids and needs extensive technician training. One BBR test only provides grade verification and another test at a lower temperature is needed to provide the continuous PG. To improve the low-temperature PG determination, Pavement Systems has introduced iCCL (incremental creep for cracking at low-temperature) test on a DSR (dynamic shear rheometer). iCCL provides equivalent results to BBR, yet requires less time to conduct, has higher precision, provides higher safety from eliminating chemicals, requires minimal technician training, and may be used in the field; hence, iCCL is a more practical than BBR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".