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  • Parametric study on the Prediction of Wave-induced Liquefaction using an Artificial Neural Network Model

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    Author(s)
    Zhang, Hong
    Jeng, Dong-Sheng
    Cha, Fred
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Cha, Fred
    Zhang, Hong
    Year published
    2007
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    Abstract
    The prediction of the wave-induced liquefaction potential is particularly important for coastal engineers involved in the design of marine structures. An artificial neural network (ANN) model is used to estimate the waveinduced liquefaction in terms of wave and seabed sediment conditions. The sensitivity of wave and seabed sediment parameters is extensively investigated to get the most accurate results. The deterministic wave and liquefaction models are used to explain the parameter features physically. Numerical examples demonstrate the capacity of the ANN modelling approach in simulating complex mechanisms such as wave-induced ...
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    The prediction of the wave-induced liquefaction potential is particularly important for coastal engineers involved in the design of marine structures. An artificial neural network (ANN) model is used to estimate the waveinduced liquefaction in terms of wave and seabed sediment conditions. The sensitivity of wave and seabed sediment parameters is extensively investigated to get the most accurate results. The deterministic wave and liquefaction models are used to explain the parameter features physically. Numerical examples demonstrate the capacity of the ANN modelling approach in simulating complex mechanisms such as wave-induced liquefaction with adequate information.
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    Journal Title
    Journal of Coastal Research
    Volume
    SI 50
    Issue
    SI 50
    Publisher URI
    http://www.cerf-jcr.org/
    Copyright Statement
    © 2007 CERF. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
    Subject
    Earth Sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/17946
    Collection
    • Journal articles

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