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Author:

Fang, G. (Fang, G..) | Liu, Z. (Liu, Z..) | Pang, W. (Pang, W..) | Zhao, L. (Zhao, L..) | Xu, K. (Xu, K..) | Cao, S. (Cao, S..) | Ge, Y. (Ge, Y..)

Indexed by:

Scopus SCIE

Abstract:

The gust factor commonly is used in wind engineering community to convert mean wind speeds into gusty winds, which exhibit significant variability in real observations of typhoon winds. This study proposes a probabilistic gust factor model that accounts for uncertainties of wind speed statistics. The statistical characteristics of wind speed from nine typhoons, including the mean, standard deviation, skewness, kurtosis, power spectral density (PSD) parameter, peak factor, and gust factor, were examined. The effects of nonstationary characteristics in terms of time-varying mean wind speed and non-Gaussian attributes of fluctuating winds on the gust factor are discussed. These wind speed statistics were incorporated into a non-Gaussian moment-based translation model to perform the Monte Carlo simulation of peak factor and gust factor. The simulation results for different gust durations were juxtaposed with observations to substantiate the accuracy of the probabilistic model. Subsequently, a standardization framework for estimating site-specific probabilistic gust factor curves was developed. This approach was applied to determine the gust typhoon wind speed hazard curve at a real bridge site with flat open terrain. The present model enables the consideration of gust factor dispersion to achieve a probabilistic gust wind hazard curve and facilitate the development of performance-based wind engineering. © 2023 American Society of Civil Engineers.

Keyword:

Typhoon Uncertainty quantification Gust factor Nonstationary Non-Gaussian Probabilistic model

Author Community:

  • [ 1 ] [Fang G.]State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai, 200092, China
  • [ 2 ] [Liu Z.]State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai, 200092, China
  • [ 3 ] [Pang W.]Glenn Dept. of Civil Engineering, Clemson Univ., Clemson, 29634, SC, United States
  • [ 4 ] [Zhao L.]State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai, 200092, China
  • [ 5 ] [Xu K.]Key Laboratory of Urban Security and Disaster Engineering, Ministry of Education, Beijing Univ. of Technology, Beijing, 100124, China
  • [ 6 ] [Cao S.]State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai, 200092, China
  • [ 7 ] [Ge Y.]State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai, 200092, China

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Source :

Journal of Structural Engineering (United States)

ISSN: 0733-9445

Year: 2024

Issue: 1

Volume: 150

4 . 1 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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