Development of a model of total building procurement costs for construction clients

Roy Duff, Margaret Emsley, Michael Gregory, David Lowe, Jack Masterman, W Hughes (Editor)

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Abstract

There is a dearth of information on the comparative costs of projects carried out using the main procurement building systems. This paper reports the feasibility study of a research programme to produce a computer-based neural network cost model to show the effect on client costs of ‘inter alia’ using different procurement approaches. A literature search identified 39 cost-significant project variables. Data were collected from collaborating QS practices, resulting in 46 project data-sets with which to test various modelling approaches. Evaluation of the data and model objectives identified multiple regression and neural networks as potential model forms. Investigation and trials of both have shown that regression and neural networks can provide effective representation of the client costs model but neural networks, due to their greater ability in modelling interdependencies between input variables, modelling non-linear relationships, and handling incomplete data sets, will probably be the better choice with which to analyse the very much larger volume of data planned for the next phase of the research. The results have demonstrated that such a model can be developed, that data to support it can be obtained and, additionally, that the utility of the model may be significantly greater than had been envisaged at the start of the study.
Original languageEnglish
Title of host publicationProceedings of the 14th Annual ARCOM Conference
EditorsW Hughes
Place of PublicationReading, UK
PublisherAssociation of Researchers in Construction Management
Pages210-218
Number of pages9
Volume1
ISBN (Print)0-9534161-0-0
Publication statusPublished - 1998
Event14th Annual ARCOM Conference - University of Reading, UK
Duration: 9 Sept 199811 Sept 1998

Conference

Conference14th Annual ARCOM Conference
CityUniversity of Reading, UK
Period9/09/9811/09/98

Keywords

  • Cost modelling, Early stage estimating, Neural networks, Procurement

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