IDEAS home Printed from https://ideas.repec.org/a/ids/ijrsaf/v12y2018i1-2p2-23.html
   My bibliography  Save this article

Uncertainty assessment in the results of inverse problems: applied to damage detection in masonry dams

Author

Listed:
  • Long Nguyen-Tuan
  • Carsten Koenke
  • Volker Bettzieche
  • Tom Lahmer

Abstract

In this work, we study the uncertainties in the results of inverse problems. The inverse problems solve damage identification problems in multifield-multiphase problems for fluid-flow problems in deforming porous materials under non-isothermal boundary conditions. These analyses are important within the structural health monitoring of masonry dams. Results of the inverse problems show a scatter due to different sources of uncertainties in model parameters, measurement data, field of measurements, and in the solving algorithms of the inverse problem. In order to see and analyse the scatter, the inverse problem is solved repeatedly by a sampling process. The uncertainty in the inverse solutions can be quantified by their probability distributions according to the sampling results.

Suggested Citation

  • Long Nguyen-Tuan & Carsten Koenke & Volker Bettzieche & Tom Lahmer, 2018. "Uncertainty assessment in the results of inverse problems: applied to damage detection in masonry dams," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 12(1/2), pages 2-23.
  • Handle: RePEc:ids:ijrsaf:v:12:y:2018:i:1/2:p:2-23
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=92498
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.

    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:ids:ijrsaf:v:12:y:2018:i:1/2:p:2-23. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=98 .

    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.