Margaret W. Emsley David J. Lowe A. Roy Duff Anthony Harding Adam Hickson
Abstract
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.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 20 (2002) Issue (Month): 6 (September) Pages: 465-472 Download reference. The following formats are available: HTML
(with abstract),
plain text
(with abstract),
BibTeX,
RIS (EndNote, RefMan, ProCite),
ReDIF
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)