J
J.Y. Fu
Researcher at City University of Hong Kong
Publications - 8
Citations - 392
J.Y. Fu is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Wind tunnel & Wind direction. The author has an hindex of 7, co-authored 8 publications receiving 344 citations. Previous affiliations of J.Y. Fu include Jinan University & Shantou University.
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Full-scale measurements of wind effects on the Jin Mao building
TL;DR: In this article, the authors presented some selected results obtained from the full-scale measurements of wind effects on the Jin Mao Building during the passage of Typhoon Rananim in August, 2004.
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Wind tunnel and full-scale study of wind effects on China’s tallest building
TL;DR: In this article, the results from a combined wind tunnel and full-scale study of wind effects on the super tall building were presented and discussed, and a detailed comparative study was conducted to investigate the influences of incident wind direction, upstream terrain conditions, and interferences from the surroundings on the wind effect on the building.
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Typhoon effects on super-tall buildings
TL;DR: In this paper, a detailed analysis of the field data was conducted to investigate the characteristics of typhoon-generated wind and wind-induced vibrations of these super-tall buildings under typhoon conditions.
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Field measurements of boundary layer wind characteristics and wind-induced responses of super-tall buildings
TL;DR: In this article, Wang et al. presented field measurement results of boundary layer wind characteristics over typical open country and urban terrains and wind-induced responses of two super-tall buildings during the passages of windstorms.
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Prediction of wind loads on a large flat roof using fuzzy neural networks
TL;DR: In this article, an Fuzzy neural network (FNN) was used to predict wind loads on buildings on the basis of data obtained from model tests in wind tunnels, where simultaneous pressure measurements were made on a large flat roof model in a boundary layer wind tunnel, and parts of the wind tunnel test results were used as the training sets for the FNN to recognize the pressure distribution patterns.