Share:


Construction projects management effectiveness modelling with neural networks

    Rasa Apanavičienė Affiliation
    ; Arvydas Juodis Affiliation

Abstract

The paper deals with important aspects of construction management key factors identification and their relative significance for the construction projects management effectiveness. The approach of artificial neural network allows the construction projects management effectiveness model to be built and to determine the key determinants from a host of possible management factors that influence the project effectiveness in terms of budget performance. A list of construction management factors was collected according to the results of past research and opinion of experienced construction management practitioners. A survey questionnaire was compiled and distributed to construction management companies in Lithuania and the USA. The historical data of construction projects performance have been used to build the neural network model. Altogether twelve key construction management factors were identified covering areas related to the project manager, project team, project planning, organization and control. Based on these factors, the construction projects management effectiveness model was established. The application algorithm of that model is presented.


The established neural network model can be used during competitive bidding process to evaluate management risk of construction project and predict construction cost variation. The model allows the construction projects managers to focus on the key success factors and reduce the level of construction risk. The model can serve as the framework for further development of the construction management decision support system.


First Published Online: 26 Jul 2012

Keyword : construction projects management, artificial neural networks

How to Cite
Apanavičienė, R., & Juodis, A. (2003). Construction projects management effectiveness modelling with neural networks. Journal of Civil Engineering and Management, 9(1), 59-67. https://doi.org/10.3846/13923730.2003.10531302
Published in Issue
Mar 31, 2003
Abstract Views
34
PDF Downloads
79