Assessment of Acoustical Measurement Methods and Standards on Rubber Asphalt Roads
Bibliographic record
Abstract
This paper focuses on the availability of reliable and widely recognized standards for measuring the tyre/pavement noise by determining the existence for a common or certified standard for measuring the asphalt rubber road noise and the possibilities of establishing a common standard or making enhancement to the current standard for accurately measuring the noise. A noise measurement study is conducted using one of many methodologies recognized internationally on both conventional and asphalt rubber road. The noise measurement study is based on the Statistical Pass-by method which is described in detail in the International Standards Organization ISO 11819-1. Certain modifications have been made in order to suit the local environmental condition during the measurement. The most significant modification from the ISO 11819-1 is the distance of the microphone location that is used in the noise measurement from the center of the test road. The ISO 11819-1 stated the microphone position as 7.5m distance from the test road. However, in North America, 15m distance is commonly used. The proportions between noise source dimensions and microphone distance are affected in such a way as to reduce the potential difference between LAmax (maximum sound pressure level) and LAE (Single-event sound exposure level) [2]. Simulations can be done to find out the influence of the microphone distance to the accuracy and reliability of the test measurement readouts besides the advantages and the disadvantages on using both microphone distances from the test road. To further prove the reliability of the study, the results are then analyzed and compared to the predicted noise level using the Traffic Noise Model (TNM) developed by the Federal Highway Administration (FHWA). The FHWA's TNM that computes highway traffic noise is constructed based on the large amount of vehicle noise-emissions database and has been made comparisons to at least five other different model results or real noise measurement study to verify the accuracy of the model.
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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 | 0.055 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".