The vibration-based structural health monitoring has been traditionally implemented through the deterministic approach that\r\nrelies on a single model to identify model parameters that represent damages. When such approach is applied for truss bridges,\r\ntruss joints are usually modeled as either simple hinges or rigid connections. The former could lead to model uncertainties due to\r\nthe discrepancy between physical configurations and their mathematical models, while the latter could induce model parameter\r\nuncertainties due to difficulty in obtaining accurate model parameters of complex joint details. This paper is to present a new\r\nperspective for addressing uncertainties associated with truss joint configurations in damage identification based on Bayesian\r\nprobabilistic model updating and model class selection. A new sampling method of the transitional Markov chain Monte Carlo is\r\nincorporated with the structure�s finite element model for implementing the approach to damage identification of truss structures.\r\nThis method can not only drawsampleswhich approximate the updated probability distributions of uncertainmodel parameters but\r\nalso provide model evidence that quantify probabilities of uncertain model classes. The proposed probabilistic framework and its\r\napplicability for addressing joint uncertainties are illustrated and examined with an application example. Future research directions\r\nin this field are discussed.
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