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  • An optimised product-unit neural network with a novel PSO–BP hybrid training algorithm: Applications to load–deformation analysis of axially loaded piles

    Author(s)
    Ismail, A
    Jeng, D-S
    Zhang, LL
    Griffith University Author(s)
    Jeng, Dong-Sheng
    Year published
    2013
    Metadata
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    Abstract
    In general, neural network training is a nonlinear multivariate optimisation problem. Unlike previous studies, in the present study, particle swarm optimisation (PSO) and back-propagation (BP) algorithms were coupled to develop a robust hybrid training algorithm with both local and global search capabilities. To demonstrate the capacity of the proposed model, we applied the model to the predictions of the load-deformation behaviour of axially loaded piles. This is a soil-structure interaction problem, involving a complex mechanism of load transfer from the pile to the supporting geologic medium. A database of full scale pile ...
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    In general, neural network training is a nonlinear multivariate optimisation problem. Unlike previous studies, in the present study, particle swarm optimisation (PSO) and back-propagation (BP) algorithms were coupled to develop a robust hybrid training algorithm with both local and global search capabilities. To demonstrate the capacity of the proposed model, we applied the model to the predictions of the load-deformation behaviour of axially loaded piles. This is a soil-structure interaction problem, involving a complex mechanism of load transfer from the pile to the supporting geologic medium. A database of full scale pile loading tests is used to train and validate the product-unit network. The results show that the proposed hybrid learning algorithm simulates the load-deformation curve of axially loaded piles more accurately than other BP, PSO, and existing PSO-BP hybrid methods. The network developed using the proposed algorithm also turns out to be more accurate than hyperbolic and t-z models.
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    Journal Title
    Engineering Applications of Artificial Intelligence
    Volume
    26
    Issue
    10
    DOI
    https://doi.org/10.1016/j.engappai.2013.04.007
    Subject
    Civil Geotechnical Engineering
    Information and Computing Sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/56352
    Collection
    • Journal articles

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