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.
|Title of host publication||Proceedings of the 14th Annual ARCOM Conference|
|Place of Publication||United Kingdom|
|Number of pages||9|
|Publication status||Published - 1998|
- Cost modelling
- Early stage estimating
- Neural networks