Contribution to the Exposure Assessment for the Evaluation of Wind Effects on Buildings
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».