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Damage Evaluation of Bridge Hanger Based on Bayesian Inference: Analytical Model
Author(s) -
Yang Ding,
Jingliang Dong,
Tonglin Yang,
Zhongping Wang,
Shuangxi Zhou,
Yongqi Wei,
Anming She
Publication year - 2021
Publication title -
advances in materials science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 42
eISSN - 1687-8442
pISSN - 1687-8434
DOI - 10.1155/2021/9947727
Subject(s) - markov chain monte carlo , bridge (graph theory) , bayes' theorem , bayesian inference , bayesian probability , inference , computer science , markov chain , sampling (signal processing) , metropolis–hastings algorithm , machine learning , artificial intelligence , medicine , filter (signal processing) , computer vision
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.

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