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    Artificial Neural Networks Incorporating Cost Significant Items towards Enhancing Estimation for (life-cycle) Costing of Construction Projects

    197275_109471_80136.pdf (877.5Kb)
    Access Status
    Open access
    Authors
    Alqahtani, Ayedh
    Whyte, Andrew
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Alqahtani, Ayedh and Whyte, Andrew. 2013. Artificial Neural Networks Incorporating Cost Significant Items towards Enhancing Estimation for (life-cycle) Costing of Construction Projects. Australasian Journal of Construction Economics and Building. 13 (3): pp. 51-64.
    Source Title
    Australasian Journal of Construction Economics and Building (Online)
    Additional URLs
    http://epress.lib.uts.edu.au/journals/index.php/AJCEB/article/view/3363
    ISSN
    1837-9133
    Remarks

    This article is published under the Open Access publishing model and distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ Please refer to the licence to obtain terms for any further reuse or distribution of this work.

    URI
    http://hdl.handle.net/20.500.11937/16292
    Collection
    • Curtin Research Publications
    Abstract

    Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective LCCA comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the whole-cost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANN is a powerful means to handle non-linear problems and subsequently map relationships between complex input/output data and address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method using MATLAB SOFTWARE; and secondly, spread-sheet optimisation using Microsoft Excel Solver. The best network used 19 hidden nodes, with the tangent sigmoid used as a transfer function for both methods. The results is that in both models, the accuracy of the developed NN model is 1% (via Excel-solver) and 2% (via back-propagation) respectively.

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