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

    Regressive approach for predicting bearing capacity of bored piles from cone penetration test data

    244381_244381.pdf (552.4Kb)
    Access Status
    Open access
    Authors
    Alkroosh, I.
    Bahadori, M.
    Nikraz, Hamid
    Bahadori, A.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Alkroosh, I. and Bahadori, M. and Nikraz, H. and Bahadori, A. 2015. Regressive approach for predicting bearing capacity of bored piles from cone penetration test data. Journal of Rock Mechanics and Geotechnical Engineering. 7 (5): pp. 584-592.
    Source Title
    Journal of Rock Mechanics and Geotechnical Engineering
    DOI
    10.1016/j.jrmge.2015.06.011
    ISSN
    1674-7755
    School
    Department of Civil Engineering
    Remarks

    This open access article is distributed under the Creative Commons license http://creativecommons.org/licenses/by-nc-nd/4.0/

    URI
    http://hdl.handle.net/20.500.11937/3435
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. In this study, the least square support vector machine (LSSVM) algorithm was applied to predicting the bearing capacity of bored piles embedded in sand and mixed soils. Pile geometry and cone penetration test (CPT) results were used as input variables for prediction of pile bearing capacity. The data used were collected from the existing literature and consisted of 50 case records. The application of LSSVM was carried out by dividing the data into three sets: a training set for learning the problem and obtaining a relationship between input variables and pile bearing capacity, and testing and validation sets for evaluation of the predictive and generalization ability of the obtained relationship. The predictions of pile bearing capacity by LSSVM were evaluated by comparing with experimental data and with those by traditional CPT-based methods and the gene expression programming (GEP) model. It was found that the LSSVM performs well with coefficient of determination, mean, and standard deviation equivalent to 0.99, 1.03, and 0.08, respectively, for the testing set, and 1, 1.04, and 0.11, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the LSSVM was accurate in predicting the pile bearing capacity. The results of comparison also showed that the proposed algorithm predicted the pile bearing capacity more accurately than the traditional methods including the GEP model.

    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 modeling load-settlement response of axially loaded (steel) driven piles
      Shahin, Mohamed (2013)
      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, ...
    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.