IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v113y2017icp363-374.html
   My bibliography  Save this article

Optimal experimental design on the loading frequency for a probabilistic fatigue model for plain and fibre-reinforced concrete

Author

Listed:
  • Rivas-López, M.J.
  • Yu, R.C.
  • López-Fidalgo, J.
  • Ruiz, G.

Abstract

The objective is to improve the fatigue characterisation process based on the concept of optimal experimental design. This is carried out through a probabilistic model, previously developed, which takes into account the experimentally observed loading frequency effect on the fatigue life in plain and fibre-reinforced concrete. The Fisher Information Matrix is first obtained for the simplified fatigue model. The optimal design is found to be located at the minimum values allowed for both the maximum stress and stress ratio, whereas the two loading frequencies are the minimum and maximum values in the defined range. Next, the FIM is derived for the extended fatigue model. The previously carried out experimental plan is 65% efficient compared to the optimum. Even though it has been developed for the specific chosen fatigue model, the current procedure can be applied to any other fatigue model to significantly improve the fatigue characterisation process of any material.

Suggested Citation

  • Rivas-López, M.J. & Yu, R.C. & López-Fidalgo, J. & Ruiz, G., 2017. "Optimal experimental design on the loading frequency for a probabilistic fatigue model for plain and fibre-reinforced concrete," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 363-374.
    • Handle: RePEc:eee:csdana:v:113:y:2017:i:c:p:363-374
    DOI: 10.1016/j.csda.2016.08.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947316302031
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2016.08.014?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. J. López‐Fidalgo & C. Tommasi & P. C. Trandafir, 2007. "An optimal experimental design criterion for discriminating between non‐normal models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 231-242, April.
    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. Elham Yousefi & Luc Pronzato & Markus Hainy & Werner G. Müller & Henry P. Wynn, 2023. "Discrimination between Gaussian process models: active learning and static constructions," Statistical Papers, Springer, vol. 64(4), pages 1275-1304, August.
    2. S. G. J. Senarathne & C. C. Drovandi & J. M. McGree, 2020. "Bayesian sequential design for Copula models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 454-478, June.
    3. Dette, Holger & Titoff, Stefanie, 2008. "Optimal discrimination designs," Technical Reports 2008,06, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Santiago Campos-Barreiro & Jesús López-Fidalgo, 2015. "D-optimal experimental designs for a growth model applied to a Holstein-Friesian dairy farm," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 491-505, September.
    5. Woods, David C. & McGree, James M. & Lewis, Susan M., 2017. "Model selection via Bayesian information capacity designs for generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 226-238.
    6. Kira Alhorn & Holger Dette & Kirsten Schorning, 2021. "Optimal Designs for Model Averaging in non-nested Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 745-778, August.
    7. Víctor Casero-Alonso & Andrey Pepelyshev & Weng K. Wong, 2018. "A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose," Statistical Papers, Springer, vol. 59(4), pages 1307-1324, December.
    8. Jun Yu & HaiYing Wang, 2022. "Subdata selection algorithm for linear model discrimination," Statistical Papers, Springer, vol. 63(6), pages 1883-1906, December.
    9. David Mogalle & Philipp Seufert & Jan Schwientek & Michael Bortz & Karl-Heinz Küfer, 2024. "Computing T-optimal designs via nested semi-infinite programming and twofold adaptive discretization," Computational Statistics, Springer, vol. 39(5), pages 2451-2478, July.
    10. Laura Deldossi & Silvia Angela Osmetti & Chiara Tommasi, 2019. "Optimal design to discriminate between rival copula models for a bivariate binary response," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 147-165, March.
    11. Tommasi, C. & López-Fidalgo, J., 2010. "Bayesian optimum designs for discriminating between models with any distribution," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 143-150, January.
    12. Duarte, Belmiro P.M. & Wong, Weng Kee & Atkinson, Anthony C., 2015. "A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 11-24.
    13. Elisa Perrone & Andreas Rappold & Werner G. Müller, 2017. "$$D_s$$ D s -optimality in copula models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 403-418, August.

    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:csdana:v:113:y:2017:i:c:p:363-374. 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: http://www.elsevier.com/locate/csda .

    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.