Probabilistic Fatigue Assessment Based on Bayesian Learning for Wind-Excited Long-Span Bridges Installed with WASHMS

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ID: 20896
2013
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Abstract
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.
Reference Key
chen2013probabilisticinternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chen, Zhi-Wei;Wang, Xiao-Ming;
Journal international journal of distributed sensor networks
Year 2013
DOI
DOI not found
URL
https://doi.org/10.1155/2013/871368
Keywords Keywords not found

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