Prediction of the Effects of Pavement Permeability on the Traffic Noise Generated by Tire and Road Surface Interactions
Bibliographic record
Abstract
Porous pavement materials such as asphalt rubber concrete (ARC) have attracted attentions from the researchers and practitioners in road science in the recent years due to their durability and environmental advantages. The porous pavements also show advantages in traffic noise reduction. This research concentrates on an investigation of the effects of the permeability of porous and other pavement materials on the response of the noise generated by the interaction between tire and the pavement surface. Experimental tests are carried out in the field to acquire tire/road noise data for difference pavement materials with implementation of the Close-Proximity CPX method. Lab experiments are performed for examining the permeability of the pavement materials. Empirical relationship between the noise level and permeability of the pavement materials is established. Comparison for the responses of the pavement materials with different permeability is also presented. The research results provide guidance for optimal design of the microstructure of porous pavements to be used for reducing traffic noise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".