Probabilistic Fatigue Assessment Based on Bayesian Learning for Wind-Excited Long-Span Bridges Installed with WASHMS
Joint Authors
Source
International Journal of Distributed Sensor Networks
Issue
Vol. 2013, Issue - (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-09-12
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Telecommunications Engineering
Information Technology and Computer Science
Abstract EN
For long-span bridges located in wind-prone regions, it is a trend to install in situ Wind and Structural Health Monitoring System (WASHMS) for long-term real-time performance assessment.
One of the functions of the WASHMS is to provide information for the assessment of wind-induced fatigue damage.
Considering the randomness of wind, it is more reasonable to describe wind-induced fatigue damage of bridge in a probabilistic way.
This paper aims to establish a probabilistic fatigue model of fatigue damage based on Bayesian learning, and it is applied to a wind-excited long-span bridge installed with a WASHMS.
Wind information recorded by the WASHMS is utilized to come up with the joint probability density function of wind speed and direction.
A stochastic wind field and subsequently wind-induced forces are introduced into the health monitoring oriented finite element model (FEM) of the bridge to predict the statistics of stress responses in local bridge components.
Bayesian learning approach is then applied to determine the probabilistic fatigue damage model.
The Tsing Ma suspension bridge in Hong Kong and its WASHMS are finally utilized as a case study.
It shows that the proposed approach is applicable for the probabilistic fatigue assessment of long-span bridges under random wind loadings.
American Psychological Association (APA)
Chen, Zhi-Wei& Wang, Xiao-Ming. 2013. Probabilistic Fatigue Assessment Based on Bayesian Learning for Wind-Excited Long-Span Bridges Installed with WASHMS. International Journal of Distributed Sensor Networks،Vol. 2013, no. -, pp.1-8.
https://search.emarefa.net/detail/BIM-505008
Modern Language Association (MLA)
Chen, Zhi-Wei& Wang, Xiao-Ming. Probabilistic Fatigue Assessment Based on Bayesian Learning for Wind-Excited Long-Span Bridges Installed with WASHMS. International Journal of Distributed Sensor Networks Vol. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-505008
American Medical Association (AMA)
Chen, Zhi-Wei& Wang, Xiao-Ming. Probabilistic Fatigue Assessment Based on Bayesian Learning for Wind-Excited Long-Span Bridges Installed with WASHMS. International Journal of Distributed Sensor Networks. 2013. Vol. 2013, no. -, pp.1-8.
https://search.emarefa.net/detail/BIM-505008
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-505008