Determining the Low-Temperature Fracture Toughness of Asphalt Mixtures
Bibliographic record
Abstract
There has been a sustained effort in applying fracture mechanics concepts to crack formation and propagation in bituminous pavement materials. Adequate fracture resistance is an essential requirement for asphalt pavements built in the northern part of the United States and Canada, for which the prevailing failure mode is cracking due to low-temperature shrinkage stresses. The current Superpave ® specifications address this issue mainly through the use of strength tests on unnotched (smooth boundary) specimens. However, recent studies have shown the limitations of this approach and have suggested that fracture mechanics concepts, based on tests performed on notched samples, should be used instead. Research in progress at the University of Minnesota investigates the use of fracture mechanics principles to determine the low-temperature fracture properties of asphalt mixtures. A testing protocol is presented that makes it possible to obtain multiple measurements of fracture toughness as a function of crack propagation based on the compliance method to measure crack length. An increase in fracture toughness with crack length is observed, which is consistent with the behavior displayed by other brittle materials. The plateau of the curves may be representative of the asphalt concrete resistance to fracture, because the initial values can be significantly influenced by the presence of the inelastic zone at the crack tip.
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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) | 0.000 | 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 | 0.001 |
| 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".