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contributor authorJia-Xing Huang
contributor authorQiu-Sheng Li
contributor authorXu-Liang Han
date accessioned2023-11-28T00:11:22Z
date available2023-11-28T00:11:22Z
date issued8/2/2023 12:00:00 AM
date issued2023-08-02
identifier otherJSENDH.STENG-11303.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4294095
description abstractThis 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.
publisherASCE
titleReconstruction of Extreme Wind Pressures on Cladding of a Skyscraper during Super Typhoon Mangkhut
typeJournal Article
journal volume149
journal issue10
journal titleJournal of Structural Engineering
identifier doi10.1061/JSENDH.STENG-11303
journal fristpage04023134-1
journal lastpage04023134-19
page19
treeJournal of Structural Engineering:;2023:;Volume ( 149 ):;issue: 010
contenttypeFulltext


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