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    Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks

    199360_199360.pdf (600.2Kb)
    Access Status
    Open access
    Authors
    Shahin, Mohamed
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Shahin, M. 2014. Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks. Soils and Foundations. 54 (3): pp. 515-522.
    Source Title
    Soils and Foundations
    DOI
    10.1016/j.sandf.2014.04.015
    ISSN
    0038-0806
    Remarks

    NOTICE: this is the author’s version of a work that was accepted for publication in the journal Soils and Foundations. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in the journal Soils and Foundations, Vol.54 iss.3 (2014). DOI: http://doi.org/10.1016/j.sandf.2014.04.015

    URI
    http://hdl.handle.net/20.500.11937/35054
    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 design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load-settlement response of piles to be well predicted. However, it is well known that the actual load-settlement response of pile foundations can only be obtained by load tests carried out in-situ, which are expensive and time-consuming. In this technical note, the recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the load-settlement response of steel driven piles subjected to axial loading. The developed RNN model was 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 developed RNN model has the ability to reliably predict the load-settlement response of axially loaded steel driven piles and can thus be used by geotechnical engineers for routine design practice.

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