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CLC number: U448.25

On-line Access: 2011-08-02

Received: 2010-10-28

Revision Accepted: 2011-05-15

Crosschecked: 2011-07-04

Cited: 4

Clicked: 5715

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2011 Vol.12 No.8 P.605-615


Prediction of extreme wind velocity at the site of the Runyang Suspension Bridge

Author(s):  Yang Deng, You-liang Ding, Ai-qun Li, Guang-dong Zhou

Affiliation(s):  Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China

Corresponding email(s):   seudengyang@yahoo.com.cn, civilding@163.com

Key Words:  Extreme wind velocity, Maximum entropy theory, Probability density function, Structural health monitoring (SHM)

Yang Deng, You-liang Ding, Ai-qun Li, Guang-dong Zhou. Prediction of extreme wind velocity at the site of the Runyang Suspension Bridge[J]. Journal of Zhejiang University Science A, 2011, 12(8): 605-615.

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author="Yang Deng, You-liang Ding, Ai-qun Li, Guang-dong Zhou",
journal="Journal of Zhejiang University Science A",
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%T Prediction of extreme wind velocity at the site of the Runyang Suspension Bridge
%A Yang Deng
%A You-liang Ding
%A Ai-qun Li
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%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1000446

T1 - Prediction of extreme wind velocity at the site of the Runyang Suspension Bridge
A1 - Yang Deng
A1 - You-liang Ding
A1 - Ai-qun Li
A1 - Guang-dong Zhou
J0 - Journal of Zhejiang University Science A
VL - 12
IS - 8
SP - 605
EP - 615
%@ 1673-565X
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1000446

This paper presents a distribution free method for predicting the extreme wind velocity from wind monitoring data at the site of the Runyang Suspension Bridge (RSB), China using the maximum entropy theory. The maximum entropy theory is a rational approach for choosing the most unbiased probability distribution from a small sample, which is consistent with available data and contains a minimum of spurious information. In this paper, the theory is used for estimating a joint probability density function considering the combined action of wind speed and direction based on statistical analysis of wind monitoring data at the site of the RSB. The joint probability distribution model is further used to estimate the extreme wind velocity at the deck level of the RSB. The results of the analysis reveal that the probability density function of the maximum entropy method achieves a result that fits well with the monitoring data. Hypothesis testing shows that the distributions of the wind velocity data collected during the past three years do not obey the Gumbel distribution. Finally, our comparison shows that the wind predictions of the maximum entropy method are higher than that of the Gumbel distribution, but much lower than the design wind speed.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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