Highlighting of the viscoelastic behaviour of interfaces in asphalt pavements – a possible origin to top-down cracking
Bibliographic record
Abstract
Top-down cracking (TDC) is known to initiate and remain near the surface of bituminous pavements. The aim of this paper is twofold: (i) show experimental evidence of the viscoelastic behaviour of interface in asphalt pavements under some temperature conditions and (ii) show that taking into account such a behaviour could provide an explanation to the mechanism involved in the initiation of TDC. This paper documents the methodology used to investigate the behaviour of the upper interface from experimental tests. The mechanical response of the experimental pavement is evaluated using three models: (1) the elastic model, (2) the Huet–Sayegh viscoelastic model to account for the behaviour of asphalt layers and (3) same as 2, but with additional very thin viscoelastic layers to represent interfaces between the asphalt layers. The software ViscoRoute 2.0© is used to evaluate the stresses and the strains at different depths of the pavement. The comparison between the experimental results and the models clearly shows that model 3 is that yields the best fit. This model shows that significant tensile stresses and strains occur near the surface and at the interface between two asphalt layers. The transposition of the viscoelastic behaviour of interfaces to real traffic conditions could explain TDC as one of the damaging modes of asphalt pavements.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".