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    Predicting load-settlement relationship of driven piles in sand and mixed soils using artificial neural networks

    143993_23977_56916.pdf (2.427Mb)
    Access Status
    Open access
    Authors
    Alkroosh, Iyad
    Shahin, Mohamed
    Nikraz, Hamid
    Date
    2010
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Alkroosh, I. and Shahin, M. and Nikraz, H. 2010. Predicting load-settlement relationship of driven piles in sand and mixed soils using artificial neural networks, in Pinto, I. and Bo, M. (ed), Twin International Conferences on Geotechnical and Geo-Environmental Engineering cum (7th) Ground Improvement Techniques, pp. 163-168. Seoul, South Korea: CI-Premier.
    Source Title
    Proceedings of the 4th International Conference on Geotechnical and Geo-Environmental Engineerring
    Source Conference
    4th International Conference on Geotechnical and Geo-Environmental Engineerring
    ISBN
    978-981-08-5201-6
    Faculty
    School of Engineering
    Department of Civil Engineering
    Faculty of Science and Engineering
    URI
    http://hdl.handle.net/20.500.11937/39704
    Collection
    • Curtin Research Publications
    Abstract

    An accurate prediction of pile behaviour under axial loads is necessary for safe and cost effective design. This paper presents the development of a new model, based on artificial neural networks (ANNs), to predict the load-settlement relationship of driven piles in sand and mixed soils, and subjected to axial loads. ANNs have been recently applied to many geotechnical engineering problems and have shown to provide high degree of success. Two models are developed; one for steel piles and the other for concrete piles. The data used for ANN models development are collected from the literature and comprise a series of in-situ driven piles load tests as well as cone penetration test (CPT) results. Predictions from the ANN models are compared with the results of experimental data, and statistical analysis is conducted to verify the performance of ANN models. The results indicate that ANN models perform well and able to predict the pile load-settlement relationship quite accurately.

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