Using TSRST to Determine the Influence of Thermal Fatigue on Hot Mix Properties
Bibliographic record
Abstract
The harsh climate and extreme temperature variations in Quebec have significant effects on the performance of bituminous pavement mixtures. Transverse cracks caused by thermal contraction are generally the first cracks to appear in pavement. In order to identify and prevent contraction cracking, in the mid-1990s, the Ministere des Transports du Quebec started testing pavement mixtures with a device that was developed in Oregon as part of SHRP (Strategic Highway Research Project), the Thermal Stress Restrained Specimen Test, or TSRST. Although previous TSRST studies demonstrated that the cracking temperature for a pavement mixture was approximately the same as the low temperature for an asphalt s-value (60) = 300 MPa, hot mixes are also affected by a thermal fatigue phenomenon. Consequently, the TSRST was adapted to allow a hot mix sample to be subjected to freeze-thaw cycles in the laboratory, while keeping the sample length constant. This technical paper will present and quantify variations in transition and cracking temperatures, as well as stresses measured in hot mixes that have been subjected to thermal fatigue cycles. Tests were conducted on a densegraded hot-mix asphalt (HMA) containing binder (PG 58-28, PG 58-34, and PG 58-40) that is routinely used on Quebec roads. Conclusions drawn from the study provide an understanding of the evolution of hot mix characteristics through freeze-thaw cycles in northern climates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".