Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Artificial intelligence for modeling load-settlement response of axially loaded (steel) driven piles

    Access Status
    Fulltext not available
    Authors
    Shahin, Mohamed
    Date
    2013
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Shahin, M.A. 2013. Artificial intelligence for modeling load-settlement response of axially loaded (steel) driven piles, in P. Delage et al (ed), 18th International Conference on Soil Mechanics and Geotechnical Engineering, Sep 2-6 2013, pp. 797-800. Paris, France: Presses des Ponts.
    Source Title
    Proceedings of the 18th International Conference on Soil Mechanics and Geotechnical Engineering
    Source Conference
    The 18th International Conference on Soil Mechanics and Geotechnical Engineering
    Additional URLs
    http://www.issmge.org/images/joomd/797-800.pdf
    URI
    http://hdl.handle.net/20.500.11937/15587
    Collection
    • Curtin Research Publications
    Abstract

    The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other and the design of pile foundations should thus consider the bearing capacity and settlement in-separately. This requires the full load-settlement behavior of piles to be well predicted. However, it is well known that the actual load-settlement behavior of pile foundations can only be obtained by load tests carried out in-situ, which are expensive and time-consuming. In this paper, artificial intelligence (AI) using the recurrent neural networks (RNN) is used to develop a prediction model that can resemble the full load-settlement response of steel driven piles subjected to axial loading. The developed RNN model is calibrated and validated using several in-situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the RNN model has the ability to predict well the load-settlement response of axially loaded steel driven piles and can thus be used by geotechnical engineers for routine design practice.

    Related items

    Showing items related by title, author, creator and subject.

    • Modelling pile capacity and load-settlement behaviour of piles embedded in sand & mixed soils using artificial intelligence
      Alkroosh, Iyad Salim Jabor (2011)
      This thesis presents the development of numerical models which are intended to be used to predict the bearing capacity and the load-settlement behaviour of pile foundations embedded in sand and mixed soils. Two artificial ...
    • Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks
      Shahin, Mohamed (2014)
      The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, ...
    • Artificial intelligence for modelling load-settlement response of axially loaded bored piles
      Shahin, Mohamed (2014)
      The design of pile foundations requires reliable estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement are traditionally carried out separately. However, soil ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.