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    Mapping rework causes and effects using artificial neural networks

    Access Status
    Fulltext not available
    Authors
    Palaneeswaran, E.
    Love, Peter
    Kumaraswamy, M.
    Ng, T.
    Date
    2008
    Type
    Journal Article
    
    Metadata
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    Citation
    Palaneeswaran, E. and Love, P. and Kumaraswamy, M. and Ng, T. 2008. Mapping rework causes and effects using artificial neural networks. Building Research and Information. 36 (5): pp. 450-465.
    Source Title
    Building Research and Information
    DOI
    10.1080/09613210802128269
    ISSN
    0961-3218
    School
    School of Built Environment
    URI
    http://hdl.handle.net/20.500.11937/21344
    Collection
    • Curtin Research Publications
    Abstract

    Rework can have adverse effects on the performance and productivity of construction projects. Techniques such as artificial neural networks (ANN) are widely used for prediction and classification problems and thus can be used to map the causes and effects of rework. The traditional back propagation neural network and general regression neural network data from 112 Hong Kong construction projects are used to examine the influence of rework causes on the various project performance indicators such as cost overrun, time overrun, and contractual claims. The results from this research could be used to develop forecasting systems and appropriate intelligent decision support frameworks for enhancing performance in construction projects. Furthermore, analysis of the neural network results indicates that the general regression neural network architecture is better suited for modelling rework causes and their impacts on project performance.

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