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ESTIMATION OF LOW-OCCURRENCE WIND SPEED AT THE PEDESTRIAN LEVEL AROUND AN ISOLATED BUILDING:
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- WANG Wei
- Faculty of Engineering Sciences, Kyushu University
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- KAWAMINAMI Taishi
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University
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- SETA Koki
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University
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- IKEGAYA Naoki
- Faculty of Engineering Sciences, Kyushu University
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- OKAZE Tsubasa
- Department of Architecture and Building Engineering, Tokyo Institute of Technology
Bibliographic Information
- Other Title
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- COMPARISON OF WEIBULL DISTRIBUTION METHOD AND GRAM.CHARLIER SERIES METHOD
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Description
This study compared the accuracies of the Weibull distribution method (two-parameter Weibull and three-parameter Weibull methods, i.e., 2W and 3W methods) and Gram.Charlier series (GCS) method for estimating the low-occurrence wind speed at the pedestrian level around an isolated building. The wind speed data analyzed in this study is taken from an LES database. For the advantage of the models to predict the probability density function (PDF) of the wind speed data, the 2W, 3W and GCS methods more accurately modelled the unimodal distribution than the bimodal distribution. In addition, the highest-order GCS method in this study (i.e., GCS-6th method) exhibited better flexibility in modelling PDFs than the 2W and 3W. For estimating the low-occurrence wind speed, the 2W, 3W and i-th-order GCS methods require the mean and standard deviation, these two statistics plus the skewness, and the first-order to i-th-order statistics, respectively. For the estimation accuracy, the 2W method is better than the second-order GCS method and the 3W method is better than the third-order GCS method although the GCS-6th method is regarded as the most accuracy method in this study owing to incorporating the higher-order statistics.
Journal
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- Wind Engineering Research
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Wind Engineering Research 27 (0), 28-37, 2022
Japan Association for Wind Engineering
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Keywords
Details 詳細情報について
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- CRID
- 1390014204799385344
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- ISSN
- 24355429
- 24354384
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- Text Lang
- en
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed