Pavement noise investigation on North Carolina highways: an on-board sound intensity approach
Bibliographic record
Abstract
This paper presents the findings on tire–pavement noise on various types of pavements by using an on-board sound intensity (OBSI) method. Mitigation of traffic noise has become an increasingly important consideration for highway agencies when constructing new highways or improving the existing systems. As a competitive alternative for noise mitigation, quieter pavement may provide advantages that noise barriers do not have, or to where sound barriers are not suited. The first step in developing quieter pavement is identifying the noise levels of different types of highway pavements. To reach the ultimate goals of quieter pavement development, this research has focused on the most imperative task, i.e., to measure the noise levels of different types of pavements in North Carolina (NC). Pavement noise levels of 61 highway sites including 153 test sections around 30 counties for nine types of pavements across North Carolina have been investigated. A thorough literature review was conducted and OBSI testing equipment with sound intensity measuring process was established during this study. The results of OBSI data indicate that the tire–pavement noise levels of the six dense graded surface courses in NC are in a lower range, from 98.2 to 99.6 dBA, comparing with other dense graded surface friction courses in other states. The overall findings indicate that relatively quieter pavements have been used in North Carolina. The OBSI data collected will provide valuable information in future research for quieter pavement development and traffic noise management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".