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Effects of Prediction Error Bias on Model Calibration and Response Prediction of a 10-Story Building

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Model Validation and Uncertainty Quantification, Volume 3

Abstract

This paper investigates the application of Hierarchical Bayesian model updating to be used for probabilistic model calibration and response prediction of civil structures. In this updating framework the misfit between the identified modal parameters and the corresponding parameters of the finite element (FE) model is considered as a Gaussian distribution with unknown parameters. For response prediction, both the structural parameters of the FE model and the parameters of the misfit error functions are considered. The focus of this paper is to (1) evaluate the performance of the proposed framework in predicting the structural modal parameters at a state that the FE model is not calibrated (extrapolation from the model), and (2) study the effects of prediction error bias on the accuracy of the predicted values. The test structure considered here is a ten-story concrete building located in Utica, NY. The modal parameters of the building at its reference state were identified from ambient vibration data using the NExT-ERA system identification method. The identified modal parameters are used to calibrate parameters of the initial FE model as well as the misfit error functions. Before demolishing the building, six of its exterior walls were removed and ambient vibration measurements were also collected from the structure after wall removal. These data are not used to calibrate the model; they are only used to validate the predicted results. The model updating framework of this paper is applied to estimate the modal parameters of the building after removal of the six walls. Good agreement is observed between the model-predicted modal parameters and those identified from vibration tests.

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Acknowledgment

The study presented here is part of a project supported by the National Science Foundation (Grant No. 1430180). The collaboration of NEES@UCLA during the planning and execution stages of the experiments is sincerely acknowledged. The authors would also like to thank the New York State Department of transportation (NYSDOT) personnel and their director Andrew Roberts for allowing the execution of these tests and for their remarkable cooperation in every part of the experiment. The supports from Tufts Technology Services, and Mr. Fatih Yalniz Vice President at WSP|PB are highly appreciated. The opinions expressed in this paper are those of the authors and do not necessarily represent those of the sponsor or the collaborators.

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Authors and Affiliations

  1. Building Structures, WSP|Parsons Brinckerhoff, New York, NY, USA

    Iman Behmanesh

  2. Department of Civil and Environmental Engineering, University at Buffalo, Buffalo, NY, USA

    Seyedsina Yousefianmoghadam & Andreas Stavridis

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

    Amin Nozari & Babak Moaveni

Authors
  1. Iman Behmanesh
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  2. Seyedsina Yousefianmoghadam
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  3. Amin Nozari
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  4. Babak Moaveni
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  5. Andreas Stavridis
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Corresponding author

Correspondence to Iman Behmanesh .

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Editors and Affiliations

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

    Sez Atamturktur

  2. Kohler Company, Kohler, USA

    Tyler Schoenherr

  3. Tufts University, Medford, Massachusetts, USA

    Babak Moaveni

  4. University of Thessaly, Thessaly, Greece

    Costas Papadimitriou

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Behmanesh, I., Yousefianmoghadam, S., Nozari, A., Moaveni, B., Stavridis, A. (2016). Effects of Prediction Error Bias on Model Calibration and Response Prediction of a 10-Story Building. In: Atamturktur, S., Schoenherr, T., Moaveni, B., Papadimitriou, C. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-29754-5_28

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  • DOI: https://doi.org/10.1007/978-3-319-29754-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29753-8

  • Online ISBN: 978-3-319-29754-5

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