A Study of Traffic Noise Reduction Performance of Arc Pavement Roads and Traffic Flow
Bibliographic record
Abstract
Asphalt Rubber Concrete (ARC) pavement has shown an excellent performance of noise reduction in terns of reducing the power of air pumping, absorbing sound power, depressing carcass vibration and changing sound reflection geometry. This research is to investigate the traffic noise reduction performance of a segment of test highway with ARC pavement in Saskatchewan, Canada. Before and after the highway section was repaved, a series of traffic noise level measurements combining with traffic flow monitoring are conducted in order to compare the sound performance of ARC and conventional pavements. A relationship between the noise level and corresponding traffic flow conditions of ARC pavement is established. The energetic averaging method is employed to study the relationship between traffic noise level and traffic flow condition. The two noise levels of 24-hour's time averaged and Statistical Pass-By noise levels indicated that the ARC pavement has a better sound performance over that of conventional pavement in terms of traffic noise reduction. The traffic noise reduction applicability of ARC pavement under various traffic flow conditions is also performed in this research.
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 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) | 0.000 | 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 teacher head, 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".