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Bayesian FE Model Updating in the Presence of Modeling Errors

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Model Validation and Uncertainty Quantification, Volume 3

Abstract

A new likelihood function is proposed for probabilistic damage identification of civil structures that are usually modeled with many simplifying assumptions and idealizations. Data from undamaged and damaged states of the structure are used in the likelihood function and damage is identified through a Bayesian finite element (FE) model updating process. The new likelihood function does not require calibration of an initial FE model to a baseline/reference model and is based on the difference between damaged and healthy state data. It is shown that the proposed likelihood function can identify structural damage as accurately as two other types of likelihood functions frequently used in the literature. The proposed likelihood is reasonably accurate in the presence of modeling error, measurement noise and data incompleteness (number of modes and number of sensors). The performance of FE model updating for damage identification using the proposed likelihood is evaluated numerically at multiple levels of modeling errors and structural damage. The effects of modeling errors are simulated by generating identified modal parameters from a model that is different from the FE model used in the updating process. It is observed that the accuracy of damage identifications can be improved by using the identified modes that are less affected by modeling errors and by assigning optimum weights between the eigen-frequency and mode shape errors.

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Acknowledgements

The authors would like to acknowledge the support of this study by the National Science Foundation Grant No. 1125624 which was awarded under the Broadening Participation Research Initiation Grants in Engineering (BRIGE) program.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Tufts University, Medford, MA, USA

    Iman Behmanesh & Babak Moaveni

Authors
  1. Iman Behmanesh
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  2. Babak Moaveni
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Corresponding author

Correspondence to Babak Moaveni .

Editor information

Editors and Affiliations

  1. Civil Engineering Department, Clemson University, Clemson, North Carolina, USA

    H. Sezer Atamturktur

  2. Department of Civil and Envt. Eng., Tufts University, Medford, Massachusetts, USA

    Babak Moaveni

  3. Department of Mech. & Industrial Eng., University of Thessaly, Volos, Greece

    Costas Papadimitriou

  4. Sandia National Laboratories, Albuquerque, New Mexico, USA

    Tyler Schoenherr

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© 2014 The Society for Experimental Mechanics, Inc.

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Behmanesh, I., Moaveni, B. (2014). Bayesian FE Model Updating in the Presence of Modeling Errors. In: Atamturktur, H., Moaveni, B., Papadimitriou, C., Schoenherr, T. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-04552-8_12

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  • DOI: https://doi.org/10.1007/978-3-319-04552-8_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04551-1

  • Online ISBN: 978-3-319-04552-8

  • eBook Packages: EngineeringEngineering (R0)

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