• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Estimation of scour depth around circular piers: applications of model tree

    Thumbnail
    View/Open
    102469_1.pdf (453.2Kb)
    Author(s)
    Etemad-Shahidi, Amir
    Bonakdar, Lisham
    Jeng, D-S
    Griffith University Author(s)
    Jeng, Dong-Sheng
    Etemad Shahidi, Amir F.
    Year published
    2015
    Metadata
    Show full item record
    Abstract
    Scour around bridge piers is one of the main causes of bridge failures and is of great importance for hydraulic engineers and scientists. Prediction of the scour depth around piers is complicated, and accurate results are rarely achieved by the existing models. Recently, data mining approaches such as artificial neural networks and fuzzy inference systems have been applied successfully to predict scour depth around hydraulic structures. In this study, an alternative robust data mining approach was used for the predictions of the scour depth around piers, and the results were compared with those of three empirical approaches. ...
    View more >
    Scour around bridge piers is one of the main causes of bridge failures and is of great importance for hydraulic engineers and scientists. Prediction of the scour depth around piers is complicated, and accurate results are rarely achieved by the existing models. Recently, data mining approaches such as artificial neural networks and fuzzy inference systems have been applied successfully to predict scour depth around hydraulic structures. In this study, an alternative robust data mining approach was used for the predictions of the scour depth around piers, and the results were compared with those of three empirical approaches. Performances of developed models were tested by experimental data sets collected in laboratory experiments and field measurements, together with existing empirical approaches. Statistical measures indicate that the proposed M5′ model provides a better prediction of scour depth than the empirical approaches.
    View less >
    Journal Title
    Journal of Hydroinformatics
    Volume
    17
    Issue
    2
    DOI
    https://doi.org/10.2166/hydro.2014.151
    Copyright Statement
    © IWA Publishing 2015. This is the author-manuscript version of this paper. The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics, Volume 17, Issue 2, Pages 226-238; DOI: 10.2166/hydro.2014.151, and is available at www.iwapublishing.com
    Subject
    Civil Engineering not elsewhere classified
    Civil Engineering
    Publication URI
    http://hdl.handle.net/10072/167673
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander