A neural networks approach for cost flow forecasting

Artificial neural networks, which simulate neuronal systems of the brain, are useful methods that have attracted the attention of researchers in many disciplinary areas. They have many advantages over traditional methods in situations where the input-output relationship of the system under study is...

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Bibliographic Details
Published in:Construction management and economics 1998-07, Vol.16 (4), p.471-479
Main Authors: Boussabaine, A.H., Kaka, A.P.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Construction industry
Cost
Cost Flow
Costs
Forecasting
Modelling
Neural Networks
Studies
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Summary:Artificial neural networks, which simulate neuronal systems of the brain, are useful methods that have attracted the attention of researchers in many disciplinary areas. They have many advantages over traditional methods in situations where the input-output relationship of the system under study is not explicitly known. This paper investigates the feasibility of using neural networks for predicting the cost flow of construction projects, explains the need for cost flow forecasting, and demonstrates the limitation of the existing models. It then introduces neural networks as an alternative approach to those mathematical and statistical methods. The method used in collecting data and modelling the cost flow is described. Results of the testing are presented and discussed.
ISSN:0144-6193
1466-433X
DOI:10.1080/014461998372240