IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v209y2021ics0951832021000259.html
   My bibliography  Save this article

A conservatism index based on structural reliability and model errors

Author

Listed:
  • Gomes, Wellison José de Santana
  • Beck, André Teófilo

Abstract

Engineering models are known to be approximate representations of reality. When approximate models are employed in design, it is usually better when they are (slightly) conservative, rather than the contrary. Out of many recent publications addressing and discussing model uncertainty, we could not find any general method for classifying if a model is conservative, or not; and how conservative it is. In this paper, we propose a model conservatism index which is based on structural reliability theory and aims at quantifying the degree of conservatism of a given structural model. Computation of the proposed conservatism index requires the same data employed for computing model error statistics. The method can be employed to evaluate models used in structural design, and corrected models used in structural reliability analysis. The method is applied to several examples, involving different kinds of simplified models, and shown to accurately classify models as conservative/non-conservative. A simple practical case study is also presented, which includes a nonlinear finite element model, to show that the proposed index may be applied in real-world situations.

Suggested Citation

  • Gomes, Wellison José de Santana & Beck, André Teófilo, 2021. "A conservatism index based on structural reliability and model errors," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000259
    DOI: 10.1016/j.ress.2021.107456
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021000259
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107456?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. Slot, René M.M. & Sørensen, John D. & Sudret, Bruno & Svenningsen, Lasse & Thøgersen, Morten L., 2020. "Surrogate model uncertainty in wind turbine reliability assessment," Renewable Energy, Elsevier, vol. 151(C), pages 1150-1162.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Ebrahimi, Mehrdad & Nobahar, Elnaz & Mohammadi, Reza Karami & Noroozinejad Farsangi, Ehsan & Noori, Mohammad & Li, Shaofan, 2023. "The influence of model and measurement uncertainties on damage detection of experimental structures through recursive algorithms," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

    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. Baisthakur, Shubham & Fitzgerald, Breiffni, 2024. "Physics-Informed Neural Network surrogate model for bypassing Blade Element Momentum theory in wind turbine aerodynamic load estimation," Renewable Energy, Elsevier, vol. 224(C).
    2. Samantha M. Roth & Ben Seiyon Lee & Sanjib Sharma & Iman Hosseini‐Shakib & Klaus Keller & Murali Haran, 2023. "Flood hazard model calibration using multiresolution model output," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    3. Jannie Sønderkær Nielsen & Henrik Stensgaard Toft & Gustavo Oliveira Violato, 2023. "Risk-Based Assessment of the Reliability Level for Extreme Limit States in IEC 61400-1," Energies, MDPI, vol. 16(4), pages 1-15, February.
    4. Zhang, Ruixing & An, Liqiang & He, Lun & Yang, Xinmeng & Huang, Zenghao, 2024. "Reliability analysis and inverse optimization method for floating wind turbines driven by dual meta-models combining transient-steady responses," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Zhang, Xiaoling & Zhang, Kejia & Yang, Xiao & Fazeres-Ferradosa, Tiago & Zhu, Shun-Peng, 2023. "Transfer learning and direct probability integral method based reliability analysis for offshore wind turbine blades under multi-physics coupling," Renewable Energy, Elsevier, vol. 206(C), pages 552-565.
    6. Postnikov, Ivan, 2022. "A reliability assessment of the heating from a hybrid energy source based on combined heat and power and wind power plants," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    7. Mohammad Hassan Ranjbar & Behnam Rafiei & Seyyed Abolfazl Nasrazadani & Kobra Gharali & Madjid Soltani & Armughan Al-Haq & Jatin Nathwani, 2021. "Power Enhancement of a Vertical Axis Wind Turbine Equipped with an Improved Duct," Energies, MDPI, vol. 14(18), pages 1-16, September.
    8. Thapa, Mishal & Missoum, Samy, 2022. "Uncertainty quantification and global sensitivity analysis of composite wind turbine blades," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Song, Yupeng & Sun, Tao & Zhang, Zili, 2023. "Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions," Renewable Energy, Elsevier, vol. 212(C), pages 570-588.

    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:209:y:2021:i:c:s0951832021000259. 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.