Abstract
With the increase of the long‐span bridge, the damage of the long‐span bridge hanger has attracted more and more attention. Nowadays, the probability statistics method based on Bayes’ theorem is widely used for evaluating the damage of bridge, that is, Bayesian inference. In this study, the damage evaluation model of bridge hanger is established based on Bayesian inference. For the damage evaluation model, the analytical expressions for calculating the weights by finite mixture (FM) method are derived. In order to solve the complex analytical expressions in damage evaluation model, the Metropolis‐Hastings (MH) sampling of Markov chain Monte Carlo (MCMC) method was used. Three case studies are adopted to demonstrate the effect of the initial value and the applicability of the proposed model. The result suggests that the proposed model can evaluate the damage of the bridge hanger.
Highlights
Under the action of cyclic load, the key parts of the structure will be damaged (Zhou et al [1]; Sun and Jahangiri [2]; Gobbato et al [3])
Imam et al [6] developed a finite element model (FEM) of a typical riveted railway bridge to analyze the effect of fatigue
Di Bona et al [7] developed the Critical Risks Method (CRM) to overcome the shortcomings of traditional techniques, which considers six factors that are able to ensure its applicability to a great variety of critical infrastructures
Summary
Under the action of cyclic load, the key parts of the structure will be damaged (Zhou et al [1]; Sun and Jahangiri [2]; Gobbato et al [3]). Lam et al [22] applied the MCMC-based Bayesian model updating method to determine the probability density functions of the various interstory stiffness values. Ching et al [31] presented a new Bayesian model updating approach based on the Gibbs sampler to update the optimal estimate of the structural parameters and update the associated uncertainties. (3) e MH sampling of the MCMC method is applied to solve the complex analytical expressions in the damage evaluation model. In Bayesian inference, Bayes’ theorem is used to deduce and update properties of an underlying probability distribution with more evidence and information available by computing the posterior probability, which can be expressed by prior probability distribution and the likelihood function. Where x is random variable; g(x) is the function of x; p(x) is the probability distribution of x; E[] is the expectation of the target distribution g(x)
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