Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66410
Title: Assessment of stochastically updated finite element models using reliability indicator
Authors: Hua, XG
Wen, Q
Ni, YQ 
Chen, ZQ
Keywords: Stochastic FE model updating
Model validation
Structural reliability
Modal variability
Structural health monitoring
Issue Date: 2017
Publisher: Academic Press
Source: Mechanical systems and signal processing, 2017, v. 82, p. 217-229 How to cite?
Journal: Mechanical systems and signal processing 
Abstract: Finite element (FE) model updating techniques have been a viable approach to correcting an initial mathematical model based on test data. Validation of the updated FE models is usually conducted by comparing model predictions with independent test data that have not been used for model updating. This approach of model validation cannot be readily applied in the case of a stochastically updated FE model. In recognizing that structural reliability is a major decision factor throughout the lifecycle of a structure, this study investigates the use of structural reliability as a measure for assessing the quality of stochastically updated FE models. A recently developed perturbation method for stochastic FE model updating is first applied to attain the stochastically updated models by using the measured modal parameters with uncertainty. The reliability index and failure probability for predefined limit states are computed for the initial and the stochastically updated models, respectively, and are compared with those obtained from the 'true' model to assess the quality of the two models. Numerical simulation of a truss bridge is provided as an example. The simulated modal parameters involving different uncertainty magnitudes are used to update an initial model of the bridge. It is shown that the reliability index obtained from the updated model is much closer to true reliability index than that obtained from the initial model in the case of small uncertainty magnitude; in the case of large uncertainty magnitude, the reliability index computed from the initial model rather than from the updated model is closer to the true value. The present study confirms the usefulness of measurement-calibrated FE models and at the same time also highlights the importance of the uncertainty reduction in test data for reliable model updating and reliability evaluation.
URI: http://hdl.handle.net/10397/66410
ISSN: 0888-3270
EISSN: 1096-1216
DOI: 10.1016/j.ymssp.2016.05.020
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