Producing reasonably accurate cost estimates at the planning stage of a project important for the subsequent success of the project. The estimator has to be able to make judgement on the cost influence of a number of factors including site conditions, procurement, risks, price changes, likely scope changes or type of contract. This can shroud the estimation process in uncertainty, which has often resulted in project cost overruns. The knowledge acquisition, generalization and forecasting capabilities of Artificial Neural Networks (ANN) are explored in this pilot study to build final cost estimation models that incorporate the cost effect of some of the factors mentioned above. Data was collected on ninety-eight water-related construction projects completed in Scotland between 2007-2011. Separate cost models were developed for normalized target cost and log of target costs. Variable transformation and weight decay regularization were then explored to improve the final model’s performance. As a prototype of a wider research, the final model’s performance was very satisfactory, demonstrating ANN ability to capture the interactions between the predictor variables and final cost. Ten input variables, all readily available or measurable at the planning stages for the project, were used within a Multilayer Perceptron Architecture and a Quasi-Newton training algorithm.
History
Pagination
307-316
Location
Edinburgh, Scotland
Start date
2102-09-03
End date
2012-09-05
ISBN-13
978-0-9552390-6-9
Language
eng
Publication classification
E Conference publication, E1.1 Full written paper - refereed
Copyright notice
2012, Association of Researchers in Construction Management
Editor/Contributor(s)
Smith S
Title of proceedings
ARCOM 2012 : Proceedings of the 28th Association of Researchers in Construction Management Annual Conference
Event
Association of Researchers in Construction Management. Conference (28th : 2012 : Edinburgh, Scotland)
Publisher
ARCOM, Association of Researchers in Construction Management
Place of publication
Reading, Eng.
Series
Association of Researchers in Construction Management Conference