IDEAS home Printed from https://ideas.repec.org/a/igg/jitpm0/v5y2014i1p14-23.html
   My bibliography  Save this article

A Neural Network Model for Predicting Cost of Pre-Fabricated Housing

Author

Listed:
  • Mladen Vukomanović

    (Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia)

  • Mirsad Kararić

    (Libra Projekt, Ltd., Zagreb, Croatia)

  • Mladen Radujković

    (Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia)

Abstract

Low performance of the construction industry stresses the need for improving current practices - especially in regard to cost. In this study the authors have found a critical set of variables for predicting total cost of pre-fabricated housing. A neural network model was applied on more than 30 projects. The model relies on 17 critical cost prediction variables. Verification, on 28 buildings, showed that: 85.7% of predicted values had the deviation lower 5%, while 10.7% had the deviation lower than 10%, in relation to the actual cost. After validating the model on new data the performances were as follows: 83.8% of predicted values had the deviation lower 5%, while 12.9% had the deviation lower than 10%. Thus, using this model, construction companies can influence project performance during project early phases, and acquire more competitive position on the market.

Suggested Citation

  • Mladen Vukomanović & Mirsad Kararić & Mladen Radujković, 2014. "A Neural Network Model for Predicting Cost of Pre-Fabricated Housing," International Journal of Information Technology Project Management (IJITPM), IGI Global, vol. 5(1), pages 14-23, January.
  • Handle: RePEc:igg:jitpm0:v:5:y:2014:i:1:p:14-23
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijitpm.2014010102
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jitpm0:v:5:y:2014:i:1:p:14-23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.