Rutting Behaviour of Flexible Pavements Aggregate Bases Measured with Small-Scale Laboratory Heavy Vehicle Simulator
Bibliographic record
Abstract
Rutting of flexible pavement structures when submitted to repeated heavy-vehicle loading is a complex phenomenon. In northern environment where thick granular layers are included in flexible pavement structures to reduce frost action, most of surface rutting occurs in unbound granular materials. This long-term permanent deformation damage can be studied with heavy vehicle simulator. A small-scale laboratory heavy vehicle simulator was developed at Laval University over the last few years and this paper presents the first results obtained for the characterization of the rutting behavior of flexible pavement bases. Standard aggregate bases, as well as base layers containing 70 percent RAP and geogrid were tested. The results obtained are in good agreement with literature on permanent deformation and geogrid reinforcement. The rutting sensitivity of RAP bases is compared to standard aggregate base layer and the structure behavior improvement caused by the geogrid is also quantified. (A) For the covering abstract of this conference see ITRD record number 201211RT334E.
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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".