Evaluation of high performance noise-reducing asphalt layers using accelerated pavement testing
Bibliographic record
Abstract
An optimal wearing course should provide safety, comfort, and durability while minimizing the impact on the environment. Some of those functions are antinomic and thus difficult to integrate considering the current state of the art. A large experimental program was carried out, involving the evaluation of 10 asphalt mix designs. Two innovative acoustic mixes, plus a traditional one, were selected from this program. Lab experiments demonstrated that the grading curves and void structure of these new mixes lead to a good compromise between acoustic performance, skid resistance, durability, and rolling resistance. A full-scale experiment was then carried out on an accelerated pavement testing facility, to validate the good behavior highlighted by laboratory studies embodied by the enhancement of acoustic absorption (peak values of 0.86 and 0.78 for the two innovative acoustic mixes, 0.39 for the reference mix) for equivalent mechanical properties. After the experiment, the properties of the test sections (mechanical properties, surface roughness, skid resistance, and acoustic properties) were evaluated. The results confirmed the good durability of the mixes under traffic loading.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".