Full Text:   <2313>

Summary:  <1765>

CLC number: U441.3

On-line Access: 2020-07-13

Received: 2019-10-10

Revision Accepted: 2020-02-11

Crosschecked: 2020-06-15

Cited: 0

Clicked: 3537

Citations:  Bibtex RefMan EndNote GB/T7714


Jian Guo


-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2020 Vol.21 No.7 P.553-564


Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge

Author(s):  Yan-long Xie, Binbin Li, Jian Guo

Affiliation(s):  ZJU-UIUC Institute, Zhejiang University, Haining 314400, China; more

Corresponding email(s):   guoj@zjut.edu.cn

Key Words:  Cable-stayed sea-crossing bridge, Operational modal analysis (OMA), Bayesian modal identification, Expectation-maximization (EM) algorithm

Yan-long Xie, Binbin Li, Jian Guo. Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge[J]. Journal of Zhejiang University Science A, 2020, 21(7): 553-564.

@article{title="Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge",
author="Yan-long Xie, Binbin Li, Jian Guo",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge
%A Yan-long Xie
%A Binbin Li
%A Jian Guo
%J Journal of Zhejiang University SCIENCE A
%V 21
%N 7
%P 553-564
%@ 1673-565X
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1900511

T1 - Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge
A1 - Yan-long Xie
A1 - Binbin Li
A1 - Jian Guo
J0 - Journal of Zhejiang University Science A
VL - 21
IS - 7
SP - 553
EP - 564
%@ 1673-565X
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1900511

Sea-crossing bridges have attracted considerable attention in recent years as an increasing number of projects have been constructed worldwide. Situated in the coastal area, sea-crossing bridges are subjected to a harsh environment (e.g. strong winds, possible ship collisions, and tidal waves) and their performance can deteriorate quickly and severely. To enhance safety and serviceability, it is a routine process to conduct vibration tests to identify modal properties (e.g. natural frequencies, damping ratios, and mode shapes) and to monitor their long-term variation for the purpose of early-damage alert. operational modal analysis (OMA) provides a feasible way to investigate the modal properties even when the cross-sea bridges are in their operation condition. In this study, we focus on the OMA of cable-stayed bridges, because they are usually long-span and flexible to have extremely low natural frequencies. It challenges experimental capability (e.g. instrumentation and budgeting) and modal identification techniques (e.g. low frequency and closely spaced modes). This paper presents a modal survey of a cable-stayed sea-crossing bridge spanning 218 m+620 m+218 m. The bridge is located in the typhoon-prone area of the northwestern Pacific Ocean. Ambient vibration data was collected for 24 h. A Bayesian fast Fourier transform modal identification method incorporating an expectation-maximization algorithm is applied for modal analysis, in which the modal parameters and associated identification uncertainties are both addressed. Nineteen modes, including 15 translational modes and four torsional modes, are identified within the frequency range of [0, 2.5 Hz].


目的:由于地理位置特殊,跨海大桥周围的环境非常复杂,进而导致跨海桥梁的模态特征复杂多变. 本文旨在应用期望最大化贝叶斯快速傅里叶变换(FFT)算法对跨海斜拉桥进行运营模态分析.
创新点:1. 通过使用期望最大化贝叶斯FFT算法,使得基于贝叶斯的运营模态分析速度更快且收敛性更高; 2. 成功识别了2.5 Hz以内的19阶模态的自然频率、阻尼比以及振型,同时得到了识别参数的不确定性大小.
结论:应用期望最大化贝叶斯FFT算法能够高效地识别2.5 Hz以内的19阶模态的自然频率、阻尼比和结构振型,并能得出参数识别的不确定性大小.

关键词:跨海斜拉桥; 运营模态分析; 期望最大化贝叶斯FFT算法

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


[1]Au SK, 2011. Fast Bayesian FFT method for ambient modal identification with separated modes. Journal of Engineering Mechanics, 137(3):214-226.

[2]Au SK, 2012. Fast Bayesian ambient modal identification in the frequency domain, Part I: posterior most probable value. Mechanical Systems and Signal Processing, 26:60-75.

[3]Au SK, 2014. Uncertainty law in ambient modal identification —Part I: theory. Mechanical Systems and Signal Processing, 48(1-2):15-33.

[4]Au SK, Zhang FL, Ni YC, 2013. Bayesian operational modal analysis: theory, computation, practice. Computers & Structures, 126:3-14.

[5]Brincker R, Ventura CE, 2015. Introduction to Operational Modal Analysis. John Wiley & Sons, Chichester, UK.

[6]Brincker R, Zhang LM, Andersen P, 2000. Modal identification from ambient responses using frequency domain decomposition. Proceedings of the International Modal Analysis Conference (IMAC), p.625-630.

[7]Brownjohn JMW, Magalhaes F, Caetano E, et al., 2010. Ambient vibration re-testing and operational modal analysis of the Humber Bridge. Engineering Structures, 32(8):2003-2018.

[8]Doebling SW, Farrar CR, Prime MB, 1998. A summary review of vibration-based damage identification methods. The Shock and Vibration Digest, 30(2):91-105.

[9]Felber AJ, 1993. Development of a Hybrid Bridge Evaluation System. PhD Thesis, University of British Columbia, Vancouver, Canada.

[10]Guo J, 2010. Current principal technique status and challenges to be confronted in construction of sea-crossing bridges. Bridge Construction, (6):66-69 (in Chinese).

[11]Guo J, Chen Y, Sun BN, 2005. Experimental study of structural damage identification based on WPT and coupling NN. Journal of Zhejiang University-SCIENCE A, 6(7):663-669.

[12]Ibrahim SR, 1977. Random decrement technique for modal identification of structures. Journal of Spacecraft and Rockets, 14(11):696-700.

[13]Kim H, Melhem H, 2004. Damage detection of structures by wavelet analysis. Engineering Structures, 26(3):347-362.

[14]Li BB, Au SK, 2019. An expectation-maximization algorithm for Bayesian operational modal analysis with multiple (possibly close) modes. Mechanical Systems and Signal Processing, 132:490-511.

[15]Li C, Li HN, Hao H, et al., 2018. Seismic fragility analyses of sea-crossing cable-stayed bridges subjected to multi-support ground motions on offshore sites. Engineering Structures, 165:441-456.

[16]Liu H, 2006. Hydrodynamic problems associated with construction of sea-crossing bridges. Journal of Hydrodynamics, Ser. B, 18(S3):13-18.

[17]Liu YC, Loh CH, Ni YQ, 2013. Stochastic subspace identification for output-only modal analysis: application to super high-rise tower under abnormal loading condition. Earthquake Engineering & Structural Dynamics, 42(4):477-498.

[18]Mevel L, Goursat M, Basseville M, 2003. Stochastic subspace-based structural identification and damage detection and localisation—application to the Z24 bridge benchmark. Mechanical Systems and Signal Processing, 17(1):143-151.

[19]Peeters B, de Roeck G, 2001. Stochastic system identification for operational modal analysis: a review. Journal of Dynamic Systems, Measurement, and Control, 123(4):659-667.

[20]Ren WX, de Roeck G, 2002. Structural damage identification using modal data. II: test verification. Journal of Structural Engineering, 128(1):96-104.

[21]Reynders E, Houbrechts J, de Roeck G, 2012. Fully automated (operational) modal analysis. Mechanical Systems and Signal Processing, 29:228-250.

[22]Sun M, Alamdari MM, Kalhori H, 2017. Automated operational modal analysis of a cable-stayed bridge. Journal of Bridge Engineering, 22(12):05017012.

[23]Taha MMR, Noureldin A, Lucero JL, et al., 2006. Wavelet transform for structural health monitoring: a compendium of uses and features. Structural Health Monitoring, 5(3):267-295.

[24]Xu SQ, Ma RJ, Wang DL, et al., 2019. Prediction analysis of vortex-induced vibration of long-span suspension bridge based on monitoring data. Journal of Wind Engineering and Industrial Aerodynamics, 191:312-324.

[25]Zhang LM, Brincker R, Andersen P, 2005. An overview of operational modal analysis: major development and issues. Proceedings of the 1st International Operational Modal Analysis Conference, p.12.

[26]Zhou Y, Sun LM, 2018. Effects of high winds on a long-span sea-crossing bridge based on structural health monitoring. Journal of Wind Engineering and Industrial Aerodynamics, 174:260-268.

[27]Zhou Y, Sun LM, 2019. Effects of environmental and operational actions on the modal frequency variations of a sea-crossing bridge: a periodicity perspective. Mechanical Systems and Signal Processing, 131:505-523.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE