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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/10772

Title: Finite element model updating of an experimental vehicle model using measured modal characteristics
Authors: Giagopoulos, Dimitrios
Ntotsios, Evangelos
Papadimitriou, Costas
Natsiavas, Sotirios
Keywords: Modal identification
Model updating
Structural identification
Multi-objective optimization
Structural dynamics
Issue Date: 2009
Publisher: National Technical University of Athens, Greece (© The authors)
Citation: GIAGOPOULOS, D. ... et al, 2009. Finite element model updating of an experimental vehicle model using measured modal characteristics. IN: Papadrakakis, M., Lagaros, N.D., Fragiadakis, M. (eds). 2nd ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2009), Rhodes, Greece, 22-24 June, pp.768-780.
Abstract: Methods for modal identification and structural model updating are employed to develop high fidelity finite element models of an experimental vehicle model using acceleration measurements. The identification of modal characteristics of the vehicle is based on ac-celeration time histories obtained from impulse hammer tests. An available modal identification software is used to obtain the modal characteristics from the analysis of the various sets of vibration measurements. A high modal density modal model is obtained. The modal characteristics are then used to update an increasingly complex set of finite element models of the vehicle. A multi-objective structural identification method is used for estimating the parameters of the finite element structural models based on minimizing the modal residu-als. The method results in multiple Pareto optimal structural models that are consistent with the measured modal data and the modal residuals used to measure the discrepancies between the measured modal values and the modal values predicted by the model. Single objective structural identification methods are also evaluated as special cases of the proposed multi-objective identification method. The multi-objective framework and the corresponding compu-tational tools provide the whole spectrum of optimal models and can thus be viewed as a gen-eralization of the available conventional methods. The results indicate that there is wide variety of Pareto optimal structural models that trade off the fit in various measured quanti-ties. These Pareto optimal models are due to uncertainties arising from model and measure-ment errors. The size of the observed variations depends on the information contained in the measured data, as well as the size of model and measurement errors. The effectiveness of the updated models and the predictive capabilities of the Pareto vehicle models are assessed.
Description: This is a conference paper.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/10772
Appears in Collections:Conference Papers (Civil and Building Engineering)

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