Abstract
Finite element (FE) model updating has emerged as a powerful technique for structural health monitoring (SHM) and damage identification (DID) of civil structures. Updating mechanics-based nonlinear FE models allows for a complete and comprehensive damage diagnosis of large and complex structures. Recursive Bayesian estimation methods, such as the Unscented Kalman filter (UKF), have been used to update nonlinear FE models of civil structures; however, their use have been limited to models with a relatively low number of degrees of freedom and with a limited number of unknown model parameters, because it is otherwise impractical for computationally demanding models with many uncertain parameters. In this paper, a FE model of the Marga-Marga bridge, an eight-span seismically-isolated bridge located in Viña del Mar-Chile, is updated based on numerically simulated response data. Initially, 95 model parameters are considered unknown, and then, based on a simplified sensitivity analysis, a total of 27 model parameters are considered in the estimation. Different measurement sets, including absolute accelerations, relative displacements, strains, and shear deformations of the isolators, are analyzed to investigate the effects of considering heterogeneous responses on the estimation results. In addition, a non-recursive estimation procedure is presented and its effectiveness in reducing the computational cost, while maintaining accuracy and robustness in the estimation, is demonstrated.
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Acknowledgements
R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT), through FONDECYT research grant No. 11160009.
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Astroza, R., Barrientos, N., Li, Y., Saavedra Flores, E. (2020). Calibration of a Large Nonlinear Finite Element Model of a Highway Bridge with Many Uncertain Parameters. In: Barthorpe, R. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-12075-7_20
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DOI: https://doi.org/10.1007/978-3-030-12075-7_20
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