Flexible pavement with SMA as an anti-fatigue layer
Bibliographic record
Abstract
Asphalt Pavement Alliance has defined the perpetual pavement as ˝an asphalt pavement designed and built to last longer than 50 years without requiring major structural rehabilitation or reconstruction and needing only periodic surface renewal…˝. The perpetual pavement design approach assumes that one can design against certain types of failure or distress by choosing the right materials and layer thicknesses. This can be achieved by providing enough stiffness in the upper pavement layers to preclude rutting and enough total pavement thickness and flexibility in the lowest layer to avoid fatigue cracking from the bottom of the pavement structure. One way to reduce the bottom up fatigue cracking in pavement structure is to add an additional anti-fatigue layer to standard asphalt layers. This layer can be an extra layer that increases the total asphalt layers thickness, or it can be layer separated from the standard asphalt base layer by reducing its thickness. The presented research aimed to evaluate the suitability of application, Croatia traditionally used asphalt mixtures within the concept of perpetual pavements. Among traditional asphalt mixtures, the stone mastic asphalt was selected as a mixture for the anti-fatigue layer. The analysis was carried out for proposed perpetual pavements of different thicknesses and/or position of stone mastic asphalt anti-fatigue layer. Calculation of pavement layers stresses and strains was done in CIRCLY software, taking into account the seasonal variations in asphalt layers properties. The analyses have shown that the addition of stone mastic asphalt layer as an anti-fatigue layer can extend flexible pavement design life.
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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 | 0.001 | 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 | 0.000 |
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".