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Advances in Civil Engineering
Volume 2013 (2013), Article ID 307171, 13 pages
Research Article

Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties

Department of Civil and Environmental Engineering, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17068, Jackson, MS 39217, USA

Received 25 March 2013; Accepted 18 July 2013

Academic Editor: Andreas Kappos

Copyright © 2013 Wei Zheng and Yi Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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