Investigation of Low Temperature Cracking in Asphalt Pavements National Pooled Fund Study 776
Bibliographic record
Abstract
Good fracture properties are an essential requirement for asphalt pavements built in the northern part of the US and in Canada for which the predominant failure mode is cracking due to high thermal stresses that develop at low temperatures. Currently, there is no agreement with respect to what experimental methods and analyses approaches to use to investigate the fracture resistance of asphalt materials and the fracture performance of asphalt pavements. This report presents a comprehensive research effort in which both traditional and new experimental protocols and analyses were applied to a statistically designed set of laboratory prepared specimens and to field samples from pavements with well documented performance to determine the best combination of experimental work and analyses to improve the low temperature fracture resistance of asphalt pavements. \n \nThe two sets of materials were evaluated using current testing protocols, such as creep and strength for asphalt binders and mixtures as well as newly developed testing protocols, such as the disk compact tension test, single edge notched beam test, and semi circular bend test. Dilatometric measurements were performed on both asphalt binders and mixtures to determine the coefficient of thermal contraction. \n \nDiscrete fracture and damage tools were utilized to model crack initiation and propagation in pavement systems using the finite element method and TCMODEL was used with the experimental data from the field samples to predict performance and compare it to the field performance data.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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 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".