Available online at www.sciencedirect.com ScienceDirect Comput. Methods Appl. Mech. Engrg. 279 (2014) 301–324 www.elsevier.com/locate/cma Model-reduction techniques for Bayesian finite element model updating using dynamic response data H.A. Jensen a,∗ , E. Millas a , D. Kusanovic a , C. Papadimitriou b a Department of Civil Engineering, Santa Maria University, Valparaiso, Chile b Department of Mechanical Engineering, University of Thessaly, GR-38334 Volos, Greece Received 30 January 2014; received in revised form 27 May 2014; accepted 23 June 2014 Available online 26 June 2014 Abstract This work presents a strategy for integrating a class of model reduction techniques into a finite element model updating formulation. In particular a Bayesian model updating approach based on a stochastic simulation method is considered in the present formulation. Stochastic simulation techniques require a large number of finite element model re-analyses to be performed over the space of model parameters during the updating process. Substructure coupling techniques for dynamic analysis are proposed to reduce the computational cost involved in the dynamic re-analyses. The effectiveness of the proposed strategy is demonstrated with identification and model updating applications for finite element building models using simulated seismic response data. c ⃝ 2014 Elsevier B.V. All rights reserved. Keywords: Bayesian updating; Dynamic response; Finite elements; Model reduction techniques; Stochastic simulation 1. Introduction Model updating using measured system response has a wide range of applications in areas such as structural response prediction, structural control, structural health monitoring, and reliability and risk assessment. [1–7]. For a proper assessment of the updated model all uncertainties involved in the problem should be considered. In fact, there always exist modeling errors and uncertainties associated with the process of constructing a mathematical model of the structure and its future excitation. Thus, the ability to quantify the uncertainties accurately and appropriately is essential for a robust prediction of future responses and reliability of structures [8,9]. In this context a fully probabilistic Bayesian model updating approach provides a robust and rigorous framework for model updating due to its ability to characterize modeling uncertainties associated with the underlying structural system [10,11]. Bayesian probabilistic tools for identifying uncertainty models as well as performing robust prediction analysis are usually based on asymptotic approximations [9,12–14] or stochastic simulation algorithms [15–20]. Asymptotic approximation methods in the Bayesian framework involve solving an optimization problem for finding the most probable model, ∗ Corresponding author. Tel.: +56 32 2654383; fax: +56 32 2654115. E-mail address: hector.jensen@usm.cl (H.A. Jensen). http://dx.doi.org/10.1016/j.cma.2014.06.032 0045-7825/ c ⃝ 2014 Elsevier B.V. All rights reserved.