In this paper, a basic and preliminary framework for the assessment of cumulative bridge cable fatigue damage due to wind-induced vibrations is presented. The damage assessment is performed using a probabilistic approach, based on a Bayesian Probabilistic Network, where the wind environment, traffic load-ing, bridge specific parameters and the mechanisms that induce significant cable vibrations are the main input parameters. The suggested framework is illustrated on a case study considering the second longest hanger ca-ble of the Great Belt Bridge outlining how information with respect to meteorological and site-specific condi-tions can be utilized to assess the probability of occurrence of ice-induced vibrations. The fatigue stress in the cable bending mode is evaluated together with the corresponding fatigue lifetime. The modeling scheme shows flexibility in the sense that the individual variables can be assigned probability structures in consistency with the available data as well as subjective knowledge. Moreover the Bayesian framework directly facilitates updating based on observations and measurements. Future efforts will be directed on inclusion of rain-wind induced vibrations and the combination of fatigue inducing bending and axial stresses.
Main Research Area:
International Symposium on Reliability Engineering and Risk Management, 2013