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    Modeling load-settlement behavior of driven piles embedded in cohesive soils using artificial neural networks

    Access Status
    Fulltext not available
    Authors
    Alkroosh, Iyad
    Nikraz, Hamid
    Date
    2011
    Type
    Conference Paper
    
    Metadata
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    Citation
    Alkroosh, I. and Nikraz, H. 2011. Modeling load-settlement behavior of driven piles embedded in cohesive soils using artificial neural networks, in Proceedings of the 14th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering: Challenges and Solutions (14th ARC 2011), May 23-27 2011. Hong Kong, China: The Hong Kong Geotechnical Society.
    Source Title
    Proceedings of the 14th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering
    Source Conference
    The 14th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering
    School
    Department of Civil Engineering
    URI
    http://hdl.handle.net/20.500.11937/29347
    Collection
    • Curtin Research Publications
    Abstract

    An accurate prediction of pile load-settlement behavior under axial load is necessary for design. This paper presents the development of a new model to predict the load-settlement behavior of pile foundations driven into cohesive soils and subjected to axial loads. Artificial neural networks (ANNs) have been utilized for this purpose. The data used for development of the ANN model is collected from the literature and comprise a series of in-situ driven piles load tests as well as cone penetration test (CPT) results. The data are divided into two subsets: Training set for model calibration and independent validation set for verification the performance of the ANN model in the real world. Sequential neural network is used for modeling. Predictions from the ANN model are compared with the results of experimental data and statistical measures are used to verify the performance of the model. The results indicate that the ANN model performs very well and able to predict the pile load-settlement relationship accurately.

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