Reconstruction of Extreme Wind Pressures on Cladding of a Skyscraper during Super Typhoon Mangkhut
Publication: Journal of Structural Engineering
Volume 149, Issue 10
Abstract
This paper proposes a novel strategy based on field measurement, wind tunnel tests, and artificial neural network (ANN) for reconstructing the wind pressures on cladding of high-rise buildings and applies it to reconstruct the extreme wind pressures on a 600-m-high skyscraper during Super Typhoon Mangkhut. In this study, four neural network models are developed for reconstruction of wind pressures on the model of the skyscraper based on wind tunnel test results. The reconstruction performance of the developed models was assessed by a proposed evaluation criterion. Then, the model with the best performance was utilized to reconstruct the extreme wind pressures on cladding of the skyscraper during Super Typhoon Mangkhut based on the wind pressure measurements at limited locations in the wind tunnel test and field measurements. The results reveal that the proposed strategy can capture the extreme wind pressures missed by the field measurements during the strong windstorm. Notably, this is the first attempt based on field measurements and wind tunnel testing to reconstruct the extreme wind pressures on cladding of a supertall building during an extreme typhoon event using the artificial intelligence technology, which aims to provide useful information for the wind-resistant cladding design of high-rise buildings.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
The work described in this paper was fully supported by a grant from the Research Grants Council of Hong Kong Special Administrative Region, China (Project No. CityU 11207519), and a grant from the National Natural Science Foundation of China (Project No. 51778554).
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Published In
Journal of Structural Engineering
Volume 149 • Issue 10 • October 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jan 14, 2022
Accepted: May 26, 2023
Published online: Aug 2, 2023
Published in print: Oct 1, 2023
Discussion open until: Jan 2, 2024
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