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This study establishes a computational framework for nonlinear finite element (FE) model updating of large-scale structures, through the exploration of two case studies pertaining to a spring-mass-damper chain model and to a laboratory vehicle with nonlinear suspensions. The proposed approach combines a substructuring model reduction approach with a near realtime system identification scheme, namely the Unscented Kalman Filter (UKF). The former aims at isolating and locally updating individual structural subsystems of a large-scale structure, while the latter, in contrast to other alternatives (e.g. the Extended Kalman Filter), offers a number of advantages in treating nonlinear systems, such as a derivative free calculation and a capacity for handling higher order nonlinearities. To this end, after formulating a detailed large-scale FE model for the vehicle frame substructure, a lumped model is adopted for the description of the nonlinear suspensions. Accordingly, a joint state and parameter estimation (JS&PE) problem is formulated on the basis of the lumped model. The proposed framework uses acceleration measurements from a limited number of sensors attached on the structure and a UKF observer for fusing these with a nonlinear FE substructure model, resulting into a JS&PE problem. The results indicate the validity of the proposed framework and motivate further implementation in large-scale structural systems with nonlinear components. 66 Available online at www.eccomasproceedia.org Eccomas Proceedia UNCECOMP (2017) 66-75 ©2017 The Authors. Published by Eccomas Proceedia. Peer-review under responsibility of the organizing committee of UNCECOMP 2017. doi: 10.7712/120217.5352.17093 M. Tsotalou, D. Giagopoulos, V. Dertimanis, E. Chatzi

MODEL UPDATING OF A NONLINEAR EXPERIMENTAL VEHICLE USING SUBSTRUCTURING AND UNSCENTED KALMAN FILTERING