Characterizing low-temperature field produced asphalt mix performance
Bibliographic record
Abstract
The Northern United States and Canada experience winter between 4-6 months of each year and thus are more prone to experience low temperature cracking as the primary distress in their asphalt pavements. This cracking results from a sudden drop in temperature or repeated freeze and thaw cycles, causing thermal stress build-up that exceeds the asphalt pavement&s;s tensile strength. Cracks may allow water infiltration into the pavement, causing moisture-induced damage, which reduces pavement life, and thus maintenance is required; this adds costs to the Department of Transportation (DOT). This research assesses the low-temperature cracking resistance of asphalt mixtures used in the State of Iowa by correlating the low-temperature performance of field-produced mix based on lab specifications. The disk-shaped compact tension (DCT) was used to evaluate low-temperature mixture fracture energy. From this Study, ten mixtures were found to have fracture energies ranging from 265.25J/m 2 to 470J/m 2 for the DCT test, where most do not meet the required fracture energy for their specified, designed levels of traffic and the minimum value of 400J/m2. Storage of asphalt as loose mixture, aging of mixture and reheating in the laboratory may have caused reduction in fracture resistance. A distress survey is recommended before the specification are revised.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".