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  • Modelling load–settlement behaviour of piles using high-order neural network (HON-PILE model)

    Author(s)
    Ismail, A
    Jeng, D-S
    Griffith University Author(s)
    Jeng, Dong-Sheng
    Year published
    2011
    Metadata
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    Abstract
    An accurate estimation of pile response to loading is a challenging task due to the complexity of the soil-pile interactions and uncertainties in the soil properties. Conventional methods of predicting pile load-settlement relationship either oversimplify the problem or require the parameters that are difficult to determine in the laboratory. In this study, a high-order neural network (HON) is developed to simulate the pile load-settlement curve using properties of the pile and SPT data along the depth of pile embedment as inputs. The results indicated a significant improvement in the quality of HON predictions over that of ...
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    An accurate estimation of pile response to loading is a challenging task due to the complexity of the soil-pile interactions and uncertainties in the soil properties. Conventional methods of predicting pile load-settlement relationship either oversimplify the problem or require the parameters that are difficult to determine in the laboratory. In this study, a high-order neural network (HON) is developed to simulate the pile load-settlement curve using properties of the pile and SPT data along the depth of pile embedment as inputs. The results indicated a significant improvement in the quality of HON predictions over that of BPN, RBF and GRNN models. Based on the comparisons with the predictions of elastic and hyperbolic models, the proposed HON model provides better predictions than existing theoretical models.
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    Journal Title
    Engineering Applications of Artificial Intelligence
    Volume
    24
    Issue
    5
    DOI
    https://doi.org/10.1016/j.engappai.2011.02.008
    Subject
    Civil Geotechnical Engineering
    Information and Computing Sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/63836
    Collection
    • Journal articles

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