Bayesian nonlinear finite element (FE) model updating using input and output measurements have emerged as a powerful technique for structural health monitoring (SHM), and damage diagnosis and prognosis of complex civil engineering systems. The Bayesian approach to model updating is attractive because it provides a rigorous framework to account for and quantify modeling and parameter uncertainty. This paper employs the unscented Kalman filter (UKF), an advanced nonlinear Bayesian filtering method, to update, using noisy input and output measurement data, a nonlinear FE model governed by a multiaxial material constitutive law. Compared to uniaxial material constitutive models, multiaxial models are typically characterized by a larger number of material parameters, thus requiring parameter estimation to be performed in a higher dimensional space. In this work, the UKF is applied to a plane strain FE model of Pine Flat dam (a concrete gravity dam on King’s River near Fresno, California) to update the time-invariant material parameters of the cap plasticity model, a three-dimensional non-smooth multi-surface plasticity concrete model, used to represent plain concrete behavior. This study considers seismic input excitation and utilizes numerically simulated measurement response data. Estimates of the multi-axial material model parameters (for the single material model used in this study) are non-unique. All sets of parameter estimates yield very similar and accurate seismic response predictions of both measured and unmeasured response quantities.
|Title of host publication||Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019|
|Publisher||Springer New York LLC|
|Number of pages||9|
|State||Published - 2020|
|Event||37th IMAC, A Conference and Exposition on Structural Dynamics, 2019 - Orlando, United States|
Duration: 28 Jan 2019 → 31 Jan 2019
|Name||Conference Proceedings of the Society for Experimental Mechanics Series|
|Conference||37th IMAC, A Conference and Exposition on Structural Dynamics, 2019|
|Period||28/01/19 → 31/01/19|
Bibliographical noteFunding Information:
Acknowledgements Funding for this work was provided by the U.S. Army Corps of Engineers through the U.S. Army Engineer Research and Development Center Research Cooperative Agreement.
© Society for Experimental Mechanics, Inc. 2020.
- Bayesian parameter estimation
- Cap plasticity model
- Concrete gravity dams
- Non-unique estimates
- Nonlinear FE model
- Unscented Kalman filter