Contribution to the Exposure Assessment for the Evaluation of Wind Effects on Buildings
Bibliographic record
Abstract
Contribution to the Exposure Assessment for the Evaluation of Wind Effects on Buildings \nJianhan Yu, Ph.D. \nConcordia University, 2022 \n \nThe upstream exposure has a major influence on wind loading and on wind environmental conditions around buildings. However, exposure characterization is also a complex and difficult wind engineering problem. The present comprehensive investigation addresses the exposure assessment issue in terms of evaluating the exposure roughness length (zo) by using various computational approaches. \n \nCountries specify exposure coefficients in wind load provisions to help designers evaluate wind loads on buildings. However, these specifications can include inconsistencies and discrepancies, leading to different results for similar cases while the wind characteristics do not change from country to country. This thesis examines the current wind load provisions of the American Society of Civil Engineers Standard (ASCE 7, 2022), the National Building Code of Canada (NBCC, 2020), the European Standard (EN 1991-1-4, 2005), the Australian/New Zealand Standard (AS/NZS 1170.2, 2021), and the National Standard of the People’s Republic of China (GB 50009, 2012) in terms of exposure, and results are compared and discussed. First, the wind load provisions of ASCE 7 (2022) are considered to illustrate the process that most of the provisions follow. For homogeneous exposure, the terrain roughness categories and the corresponding exposure factors are compared. Additionally, the suggested minimum upstream fetch length for different exposure types is discussed by comparing them with the latest research findings. For non-homogeneous exposure, equations to calculate small-scale roughness change in various provisions are assessed by comparing them with wind tunnel experimental data. The inconsistencies between different provisions are identified, and remedies are proposed to minimize or avoid various errors, which are sometimes subjective. Other approaches such as the internal boundary layer (IBL) theory-based method, the morphometric method, the anemometric method, and the geographic information system (GIS)-based method are reviewed. It was found that it is usually expensive or time-consuming to estimate the exposure coefficients through these methods, particularly in complex terrain. Therefore, an innovative approach to estimate the value of zo based on Google Earth Pro is proposed, and this approach is efficient and freely available. Two case studies, namely, London, UK and the Tampa International Airport, Florida, were adopted to verify the accuracy of the proposed method and yielded satisfactory results. \n \nThis thesis also investigates the effects of upstream exposure on environmental wind engineering problems by taking pedestrian-level wind (PLW) velocity cases as typical examples. The methodology of computational wind engineering (CWE), which works better on environmental challenges than on structural wind engineering problems, is adopted, and the expected discrepancies in the results for typical cases are established and assessed. The sensitivity of the PLW velocity factors to the upstream exposure fetch is documented and discussed. The research presented in the thesis demonstrates a great potential to contribute to further development of wind standards and codes of practice, as far as the characterization of the upstream exposure is concerned, at the national and international level (ISO).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".