Assessing the Propagation of Traffic Noise and Its Impact on High- rise Apartment Buildings Adjacent to an Urban Expressway: A Case Study in Chengdu, China
Bibliographic record
Abstract
Abstract With rapid urbanization, traffic growth has accelerated in specific Chinese cities. Due to strict urban construction land policies, many high-rise apartment buildings have been constructed near expressways. The substantial traffic volume generates significant noise pollution, negatively affecting the residents of these high-rise buildings. To gain a comprehensive understanding of how expressway traffic noise impacts the living environment of adjacent high-rise apartments, thorough field investigations and analyses have been conducted. Professional noise measuring instruments, such as the DT-8852 Sound Level Meter, were employed to assess noise levels on different floors and at various times. The propagation pattern of traffic noise was analyzed based on the measured data, taking into account factors that could influence noise propagation, such as time periods, building floors, and horizontal distance. According to the results of a questionnaire survey, approximately 64% of respondents perceive the impact of traffic noise as high or very high. Moreover, 37% and 19% of respondents believe that traffic noise significantly affects their sleep quality and mood, respectively. These survey findings indicate that traffic noise has a significant impact on the residential experience of the studied buildings. The field investigation of noise reveals that the daytime average noise LAeq on the 9th to 28th floors ranges from 61 to 67 dB on weekdays and 57 to 66 dB on weekends, which is higher compared to other floors. Meanwhile, the daytime average noise LAeq on the 4th to 8th floors during weekdays and weekends ranges from 55 to 63 dB and 52 to 63 dB, respectively. These test results suggest that the 4th to 8th floors experience less impact, while the 9th to 28th floors are more affected by the traffic noise induced by Jian-Nan Avenue. The field investigation results for horizontal noise measuring points indicate that the distance between the building and the avenue edge should be at least 42.4m (51.18m) if an indoor noise LAeq (Lmax) of less than 45 dB is desired. This finding highlights the importance of appropriate distance to mitigate the effects of traffic noise on indoor environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".