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Record W4396865246 · doi:10.3390/buildings14051377

Assessing Traffic Noise and Its Impact on High-Rise Apartment Buildings Adjacent to an Urban Expressway: A Case Study in Chengdu, China

2024· article· en· W4396865246 on OpenAlexaff
Heng Yu, Ailing Li

Bibliographic record

VenueBuildings · 2024
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsApartmentTraffic noiseNoise (video)ChinaUrbanizationNoise barrierEnvironmental scienceRoad trafficGeographyTransport engineeringCivil engineeringEngineeringComputer scienceNoise reductionEconomic growth

Abstract

fetched live from OpenAlex

With rapid urbanization, traffic growth has accelerated in some cities in China. Due to strict urban construction land utilization policies, many high-rise apartment buildings have been constructed adjacent to expressways. To better understand the impact of urban traffic noise on the residents of high residential buildings adjacent to expressways and the differences in noise impacts on different floors, on-site noise monitoring and questionnaires for building residents have been conducted. The characteristics of traffic noise were analyzed based on the measured data, and factors, including time periods and building floors, were considered. According to the results of the questionnaire survey, 56.06% of the male respondents and 54.55% of the female respondents think that the impact of traffic noise on high-rise buildings is “high” or “very high”; 50.53% of the respondents who were in “good” or “very good” condition thought that the traffic noise has a “significant impact” or “very significant impact” on their sleep and daily life. In addition, 25% of respondents living on floors 4–10 and 62.5% of respondents living on floors 11–20 considered the traffic noise to have a “significant impact” or “very significant impact” on their sleep and daily life. The on-site noise monitoring results show that the noise levels (LAeq) outside the windows of the studied buildings remain significantly elevated, with daytime noise on working days ranging from 56 to 70 dB(A), and on weekends ranging from 50 to 65 dB(A). During the four time periods on weekdays from 7 a.m. to 9 a.m., 11 a.m. to 1 p.m., 5 p.m. to 7 p.m., and 10 p.m. to 12 a.m., the average LAeq levels on floors 11–20 are higher than those on floors 4–10 by 4.04 dB(A), 4.92 dB(A), 4.06 dB(A), and 2.67 dB(A), respectively. Similarly, during these time periods on weekends, the levels on floors 11–20 are higher than those on floors 4–10 by 4.96 dB(A), 6.32 dB(A), 5.28 dB(A), and 5.24 dB(A), respectively. This indicates that floors 4–10 of the building experience relatively lower noise levels, while floors 11–20 are subjected to comparatively higher levels of noise disturbance.

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 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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.432
Teacher spread0.391 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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