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Analysis of Tire-Pavement Noise Spectrum of Noise Reduction Dense Asphalt-Rubber Pavement

2012· article· en· W1997306375 on OpenAlex
Zhao Guo, Guo Hui Shen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced engineering forum · 2012
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsNoise (video)AsphaltGeotechnical engineeringNatural rubberEnvironmental scienceNoise reductionPavement engineeringSubgradeDurabilityBeijingMaterials scienceEngineeringComposite materialAcousticsComputer scienceChina

Abstract

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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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Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.235
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it