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    Intelligent computing for predicting axial capacity of drilled shafts

    171440_171440.pdf (605.2Kb)
    Access Status
    Open access
    Authors
    Shahin, Mohamed
    Jaksa, M.
    Date
    2009
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Shahin, Mohamed A. and Jaksa, Mark B. 2009. Intelligent computing for predicting axial capacity of drilled shafts, in Iskander, Magued and Laefer, Debra F. and Hussein, Mohamad H. (ed), International Foundation Congress and Equipment Expo, IFCEE 09, Mar 15 2009, pp. 26-33. Orlando, Florida, USA: American Society of Civil Engineers (ASCE).
    Source Title
    Proceedings of the International Foundation Congress and Equipment Expo
    Source Conference
    International Foundation Congress and Equipment Expo, IFCEE 09
    DOI
    10.1061/41022(336)4
    ISBN
    978-0-7844-1022-6
    School
    Department of Civil Engineering
    Remarks

    This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers.

    URI
    http://hdl.handle.net/20.500.11937/7090
    Collection
    • Curtin Research Publications
    Abstract

    In the last few decades, numerous methods have been developed for predicting the axial capacity of drilled shafts. Among the available methods, the cone penetration test (CPT) based models have been shown to give better predictions in many situations. This can be attributed to the fact that CPT-based methods have been developed in accordance with the results of the CPT tests, which have been found to yield more reliable soil properties, hence, more accurate axial capacity predictions of drilled shafts. In this paper, one of the most commonly used artificial intelligence techniques, i.e. artificial neural networks (ANNs), was utilized in an attempt to obtain more accurate axial capacity predictions for drilled shafts. The ANN model was developed using data collected from the literature that comprise CPT results and drilled shaft load tests of 94 case records. The predictions from the ANN model were compared with those obtained from three commonly used available CPT-based methods. The results indicate that the ANN-based model provides more accurate axial capacity predictions of drilled shafts and outperforms the available conventional methods.

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