IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v187y2020i3d10.1007_s10957-020-01761-3.html
   My bibliography  Save this article

Optimal Sensors Placement in Dynamic Damage Detection of Beams Using a Statistical Approach

Author

Listed:
  • Egidio Lofrano

    (Sapienza University of Rome)

  • Marco Pingaro

    (Sapienza University of Rome)

  • Patrizia Trovalusci

    (Sapienza University of Rome)

  • Achille Paolone

    (Sapienza University of Rome)

Abstract

Structural monitoring plays a central role in civil engineering; in particular, optimal sensor positioning is essential for correct monitoring both in terms of usable data and for optimizing the cost of the setup sensors. In this context, we focus our attention on the identification of the dynamic response of beam-like structures with uncertain damages. In particular, the non-localized damage is described using a Gaussian distributed random damage parameter. Furthermore, a procedure for selecting an optimal number of sensor placements has been presented based on the comparison among the probability of damage occurrence and the probability to detect the damage, where the former can be evaluated from the known distribution of the random parameter, whereas the latter is evaluated exploiting the closed-form asymptotic solution provided by a perturbation approach. The presented case study shows the capability and reliability of the proposed procedure for detecting the minimum number of sensors such that the monitoring accuracy (estimated by an error function measuring the differences among the two probabilities) is not greater than a control small value.

Suggested Citation

  • Egidio Lofrano & Marco Pingaro & Patrizia Trovalusci & Achille Paolone, 2020. "Optimal Sensors Placement in Dynamic Damage Detection of Beams Using a Statistical Approach," Journal of Optimization Theory and Applications, Springer, vol. 187(3), pages 758-775, December.
    • Handle: RePEc:spr:joptap:v:187:y:2020:i:3:d:10.1007_s10957-020-01761-3
    DOI: 10.1007/s10957-020-01761-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-020-01761-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10957-020-01761-3?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. Marco Pingaro & Giacomo Maurelli & Paolo Venini, 2020. "Analysis and Damage Identification of a Moderately Thick Cracked Beam Using an Interdependent Locking-Free Element," Journal of Optimization Theory and Applications, Springer, vol. 187(3), pages 800-821, December.
    2. Ting-Hua Yi & Hong-Nan Li & Ming Gu, 2011. "Optimal Sensor Placement for Health Monitoring of High-Rise Structure Based on Genetic Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2011, pages 1-12, May.
    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. Oladayo S. Ajani & Member Joy Usigbe & Esther Aboyeji & Daniel Dooyum Uyeh & Yushin Ha & Tusan Park & Rammohan Mallipeddi, 2023. "Greenhouse Micro-Climate Prediction Based on Fixed Sensor Placements: A Machine Learning Approach," Mathematics, MDPI, vol. 11(14), pages 1-14, July.

    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:spr:joptap:v:187:y:2020:i:3:d:10.1007_s10957-020-01761-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.