Permanent Deformation of Various Unbound Aggregates Submitted to Seasonal Frost Conditions
Bibliographic record
Abstract
Pavement performance in northern climate as encountered in Canada is significantly influenced by frost action. When it comes to granular materials used in flexible pavement foundations, few study focused on the effect of seasonal frost conditions on the mechanical properties of such materials, mostly because such materials are usually considered as non-sensitive to such environmental solicitation. As it is well known that seasonal frost conditions may cause an increase of water content as well as a loosening of granular assemblies, the effect of frost action on the long term performance, expressed as resistance to permanent deformation, of base granular materials was measured for various aggregate sources and two gradings through a comprehensive laboratory study based on cyclic triaxial testing. Five saturated samples were used as reference and 5 samples were tested following a freeze-thaw cycle. It was found that the freeze-thaw cycle has a more important effect than grading and that it mainly influences the samples post-compaction magnitude. Moreover, following a freeze-thaw cycle, the percentage of the final permanent deformation that is related to post-compaction usually increases, as the post-compaction model parameter was found to increase and as the permanent deformation rate model parameter was found to slightly decrease.
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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 | 0.000 | 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) | 0.001 | 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".