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Structural Reliability Updating and Damage Assessment with Bayesian Networks

HAIFENG YANG, ZIYAN WU, HONGBIN SUN, PENG SUN

Abstract


The traditional reliability analysis method cannot handle discrete variables and cannot give response timely when there are a new information (usually measurements) about the structure. In this paper, a computational framework for structural reliability updating and damage assessment is proposed. System modeling of traditional Bayesian network is developed with continuous variables discretization and elimination. Firstly the reliability Bayesian network (RBN) is established according to the structure type, and to discretize the key component information variables. Then the redundant continuous nodes are eliminated according to Shachter's node elimination rule. Final the variable elimination method is used to carry out the exact inference and calculate the posterior probability distribution. When evidence (measurement) is available, at forward inference, the proposed method can facilitates structural reliability updating, and at backward inference, the proposed method can carry out damage assessment for the selected key component. Taking a rigid frame as the research object. By comparison with Monte Carlo method, the reliability deviation is less than 5%, it shows validity and accuracy of the proposed method.


DOI
10.12783/shm2017/13981

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