Data modelling and the application of a neural network approach to the prediction of total construction costs

Neural network cost models have been developed using data collected from nearly 300 building projects. Data were collected from predominantly primary sources using real-life data contained in project files, with some data obtained from the Building Cost Information Service, supplemented with further...

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Bibliographic Details
Published in:	Construction management and economics Vol. 20; no. 6; pp. 465 - 472
Main Authors:	Emsley, Margaret W., Lowe, David J., Duff, A. Roy, Harding, Anthony, Hickson, Adam
Format:	Journal Article
Language:	English
Published:	London Taylor & Francis Group 01-09-2002 Taylor and Francis Journals E. & F.N. Spon
Series:	Construction Management & Economics
Subjects:	Construction costs Cost Modelling Estimating techniques Linear Regression Analysis Neural Networks Studies
Online Access:	Get full text
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Summary:	Neural network cost models have been developed using data collected from nearly 300 building projects. Data were collected from predominantly primary sources using real-life data contained in project files, with some data obtained from the Building Cost Information Service, supplemented with further information, and some from a questionnaire distributed nationwide. The data collected included final account sums and, so that the model could evaluate the total cost to the client, clients' external and internal costs, in addition to construction costs. Models based on linear regression techniques have been used as a benchmark for evaluation of the neural network models. The results showed that the major benefit of the neural network approach was the ability of neural networks to model the nonlinearity in the data. The 'best' model obtained so far gives a mean absolute percentage error (MAPE) of 16.6%, which includes a percentage (unknown) for client changes. This compares favourably with traditional estimating where values of MAPE between 20.8% and 27.9% have been reported. However, it is anticipated that further analyses will result in the development of even more reliable models.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0144-6193 1466-433X
DOI:	10.1080/01446190210151050