IDEAS home Printed from https://ideas.repec.org/a/taf/gcmbxx/v24y2021i6p597-611.html
   My bibliography  Save this article

Leveraging machine learning for predicting human body model response in restraint design simulations

Author

Listed:
  • Hamed Joodaki
  • Bronislaw Gepner
  • Jason Kerrigan

Abstract

The objective of this study was to leverage and compare multiple machine learning techniques for predicting the human body model response in restraint design simulations. Parametric simulations with 16 independent variables were performed. Ordinary least-squares (OLS), least absolute shrinkage and selection operator (LASSO), neural network (NN), support vector regression (SVR), regression forest (RF), and an ensemble method were used to develop response surface models of the simulations. The hyperparameters of the machine learning techniques were optimized through grid search and cross-validation to avoid under-fitting and over-fitting. The ensemble method outperformed other techniques, followed by LASSO, SVR, NN, RF, and OLS. Findings indicated that optimizing the metamodel hyper-parameters are essential to predict the optimum set of restraint design parameters.

Suggested Citation

  • Hamed Joodaki & Bronislaw Gepner & Jason Kerrigan, 2021. "Leveraging machine learning for predicting human body model response in restraint design simulations," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 24(6), pages 597-611, April.
    • Handle: RePEc:taf:gcmbxx:v:24:y:2021:i:6:p:597-611
    DOI: 10.1080/10255842.2020.1841754
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10255842.2020.1841754
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10255842.2020.1841754?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.

    More about this item

    Statistics

    Access and download statistics

    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:taf:gcmbxx:v:24:y:2021:i:6:p:597-611. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/gcmb .

    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.