Stochastic Finite Element Model Updating by Bootstrapping
Conference contribution, 2016

This paper presents a new stochastic finite element model calibration framework for estimation of the uncertainty in model parameters, which combines the principles of bootstrapping with the technique of FE model calibration with damping equalization. The bootstrapping allows to quantify the uncertainty bounds on the model parameters by constructing a number of resamples, with replacement, of the experimental data and solving the FE model calibration problem on the resampled datasets. To a great extent, the success of the calibration problem depends on the starting value for the parameters. The formulation of FE model calibration with damping equalization gives a smooth metric with a large radius of convergence to the global minimum and its solution can be viewed as the initial estimate for the model parameters. In this study, practical suggestions are made to improve the performance of this algorithm in dealing with noisy measurements. The performance of the proposed stochastic calibration algorithm is illustrated using simulated data for a six degree-of-freedom mass-spring model.

Uncertainty quantification

0.632 Bootstrap

Stochastic FE model calibration

Frequency response

Experiment design


Vahid Yaghoubi Nasrabadi


Majid Khorsand Vakilzadeh

Swedish Wind Power Technology Center (SWPTC)


Anders Johansson


Thomas Abrahamsson


Model Validation and Uncertainty Quantification, vol 3. Conference Proceedings of 34th IMAC Conference and Exposition on Structural Dynamics, Orlando, Florida, JAN 25-28, 2016

2191-5652 (eISSN)


Subject Categories

Mechanical Engineering





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