WIND TUNNEL MEASUREMENT AND ASSESSMENT ON THE PEDESTRIAN WIND ENVIRONMENT – A CASE STUDY OF JINYING HIGH RISE BUILDING IN TAIPEI
Bibliographic record
Abstract
In this paper, wind tunnel measurement study on the pedestrian level (1.5 m to 2 m height from the ground) wind environment was carried out and applied to a case measurement of JinYing high-rise building of a height of 86.8 m in Taipei, Taiwan. Wind tunnel measurements incorporating with statistical analysis of in-situ recorded wind data were applied to asses the wind environment of pedestrian level wind comfort and safety due to the JinYing building project. Long term in-situ recorded wind data in the nearby of the high-rise building site was collected and analyzed. The Weibull probability distribution was found to fit better for the wind speed data. For each location around the JinYin building, we integrated the measured wind tunnel pedestrian level peak wind speed and Weibull probability distribution of the wind speed to yield the results for assessing the pedestrian level wind comfort and safety around the building. Based on the pedestrian level wind acceptability criteria proposed by the RWDI company in Canada, it is concluded the comfort and safety of pedestrian level activities are acceptable of the JinYing high rise building project. Also the present study offered an example for assessing how the high-rise building project affects the local wind environment experienced by pedestrian activities both to ensure comfort or safety and to facilitate the attractiveness of the building project.
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 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".