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Hierarchical Bayesian Model Updating for Probabilistic Damage Identification

  • Conference paper
Model Validation and Uncertainty Quantification, Volume 3

Abstract

This paper presents the newly developed Hierarchical Bayesian model updating method for identification of civil structures. The proposed updating method is suitable for uncertainty quantification of model updating parameters, and probabilistic damage identification of the structural systems under changing environmental conditions. The Bayesian model updating frameworks in the literature have been successfully used for predicting the “parameter estimation uncertainty” of model parameters with the assumption that there is no underlying inherent variability in the updating parameters. However, different sources of uncertainty such as changing ambient temperature or wind speed, and loading conditions will introduce variability in structural mass and stiffness of civil structures. The Hierarchical Bayesian model updating is capable of predicting the underlying variability of updating parameters in addition to their estimation uncertainty. This approach is applied for uncertainty quantification and damage identification of a three-story shear building model. The proposed updating framework is finally implemented for uncertainty quantification of model updating results based on experimentally measured data of a footbridge which is exposed to severe environmental conditions. In this application, the stiffness parameter of the model is estimated as a function of measured temperature through the Hierarchical framework.

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Acknowledgment

The authors would like to acknowledge partial 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. The authors would also like to acknowledge Mr. Durwood Marshall at the Tufts Technology Services for his help and support in using Tufts High-performance computing research cluster and Ms. Shirin Mardani for preparing the AutoCAD drawings.

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Authors and Affiliations

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

    Iman Behmanesh & Babak Moaveni

  2. Department of Civil Engineering, KU Leuven, Heverlee, Belgium

    Geert Lombaert

  3. Department of Mechanical Engineering, University of Thessaly, Volos, Greece

    Costas Papadimitriou

Authors
  1. Iman Behmanesh
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  2. Babak Moaveni
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  3. Geert Lombaert
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  4. Costas Papadimitriou
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Corresponding author

Correspondence to Babak Moaveni .

Editor information

Editors and Affiliations

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

    H. Sezer Atamturktur

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

    Babak Moaveni

  3. Department of Mechanical Engineering, University of Thessaly, Volos, Greece

    Costas Papadimitriou

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

    Tyler Schoenherr

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© 2015 The Society for Experimental Mechanics, Inc.

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Behmanesh, I., Moaveni, B., Lombaert, G., Papadimitriou, C. (2015). Hierarchical Bayesian Model Updating for Probabilistic Damage Identification. 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/10.1007/978-3-319-15224-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-15224-0_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15223-3

  • Online ISBN: 978-3-319-15224-0

  • eBook Packages: EngineeringEngineering (R0)

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