Flexible pavement with SMA as an anti-fatigue layer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 it