IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v195y2020ics0951832019301255.html
   My bibliography  Save this article

An effective parametric model reduction technique for uncertainty propagation analysis in structural dynamics

Author

Listed:
  • Jensen, H.A.
  • Mayorga, F.
  • Valdebenito, M.
  • Chen, J.

Abstract

An efficient formulation for uncertainty propagation analysis of complex structural models is presented. The formulation is based on parametric reduced-order models. Fixed-interface normal modes and interface modes are approximated in terms of a set of support points in the uncertain parameter space. The potential time-consuming step of computing the modes for different values of the model parameters needs to be performed only at the support points. Based on these approximate modes, reduced-order matrices can be updated efficiently during the simulation process associated with the uncertainty propagation analysis. The effectiveness of the proposed parametric model reduction technique is demonstrated by means of two numerical examples.

Suggested Citation

  • Jensen, H.A. & Mayorga, F. & Valdebenito, M. & Chen, J., 2020. "An effective parametric model reduction technique for uncertainty propagation analysis in structural dynamics," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:reensy:v:195:y:2020:i:c:s0951832019301255
    DOI: 10.1016/j.ress.2019.106723
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832019301255
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2019.106723?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jensen, H.A. & Esse, C. & Araya, V. & Papadimitriou, C., 2017. "Implementation of an adaptive meta-model for Bayesian finite element model updating in time domain," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 174-190.
    2. Jensen, H.A. & Muñoz, A. & Papadimitriou, C. & Millas, E., 2016. "Model-reduction techniques for reliability-based design problems of complex structural systems," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 204-217.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Jin-Yang & Lu, Jubin & Zhou, Hao, 2023. "Reliability analysis of structures with inerter-based isolation layer under stochastic seismic excitations," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Liu, Yushan & Li, Luyi & Chang, Zeming, 2023. "Efficient Bayesian model updating for dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    3. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2016. "Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 24-33.
    4. Å nipas, Mindaugas & Radziukynas, Virginijus & ValakeviÄ ius, Eimutis, 2018. "Numerical solution of reliability models described by stochastic automata networks," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 570-578.
    5. Yoon, Joung Taek & Youn, Byeng D. & Yoo, Minji & Kim, Yunhan, 2017. "A newly formulated resilience measure that considers false alarms," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 417-427.
    6. Jerez, D.J. & Jensen, H.A. & Beer, M., 2022. "An effective implementation of reliability methods for Bayesian model updating of structural dynamic models with multiple uncertain parameters," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Jensen, H.A. & Esse, C. & Araya, V. & Papadimitriou, C., 2017. "Implementation of an adaptive meta-model for Bayesian finite element model updating in time domain," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 174-190.
    8. Yoo, Minji & Kim, Taejin & Yoon, Joung Taek & Kim, Yunhan & Kim, Sooho & Youn, Byeng D., 2020. "A resilience measure formulation that considers sensor faults," Reliability Engineering and System Safety, Elsevier, vol. 199(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:195:y:2020:i:c:s0951832019301255. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.