Analysis of Tire-Pavement Noise Spectrum of Noise Reduction Dense Asphalt-Rubber Pavement
Bibliographic record
Abstract
The types of noise reduction asphalt pavement were summarized such as single layer porous or two-layer porous asphalt pavement, elastic asphalt pavement, optimized surface texture pavement, and universally composable one based on these three types. In Beijing China, it was very drought and short of rainfall, there were large volume of traffic, heavy wheel load and many dirt things on the pavement surface taken by the tires too. So asphalt-rubber pavement was the most common one for noise reduction, which was paved by gap-graded, macrotexture, dense asphalt concrete, belonging to the types of elastic and optimized surface texture noise reduction pavement. And it could reduce tire-pavement noise obviously and had excellent durability, All proved that this types of noise reduction pavement had gone through traffic and climate environment of Beijing well. It had measured tire-pavement noise of asphalt–rubber pavement and stone mustic asphalt pavement in Beijing from 2009 to 2012. This measurement was according to Measurement of close-proximity method，which prepared by international organization for standardization in the year 2000. And the test vehicle was a trailer for measuring tire-pavement noise which met requirements of ISO/CD 11819-2:2000. The factors effected tire-pavement noise spectrum were analysed, such as temperature, speed, age of pavement and so on. It reveals that, The tire-pavement noise sound level could get higher especially higher during the frequency 500Hz~2500Hz in the noise spectrum, while the testing speed increase, or the temperature decrease , or the age of pavement grow. while the frequency lay on the range of higher than 800 Hz, the asphalt–rubber pavement’s noise sound level were lower than the stone mustic asphalt pavement’s one in all situations, and when the temperature decreased from 30°C to 0°C , the low limit frequency decreased from 800Hz to 63 Hz . In order to show the reason , it had tested dynamic modulus and phase angle of the two kinds of pavement materials under different temperature and load frequency with the help of Simple Performance Tester, The result shows that, asphalt-rubber concrete has smaller Phase angle at wide temperature as well as frequency changes, it could be one of the main reasons to explain this phenomenon. It could provide reference for designing, constructing, maintaining and evaluating the noise reduction asphalt pavement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".