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My cost runneth over: data mining to reduce construction cost overruns.

Ahiaga-Dagbui, Dominic D.; Smith, Simon D.

Authors

Dominic D. Ahiaga-Dagbui

Simon D. Smith



Contributors

Dominic D. Ahiaga-Dagbui
Editor

Simon D. Smith
Editor

Abstract

Most construction projects overrun their budgets. Among the myriad of explanations giving for construction cost overruns is the lack of required information upon which to base accurate estimation. Much of the financial decisions made at the time of decision to build is thus made in an environment of uncertainty and oftentimes, guess work. In this paper, data mining is presented as key business tool to transform existing data into key decision support systems to increase estimate reliability and accuracy within the construction industry. Using 1600 water infrastructure projects completed between 2004 and 2012 within the UK, cost predictive models were developed using a combination of data mining techniques such as factor analysis, optimal binning and scree tests. These were combined with the learning and generalising capabilities of artificial neural network to develop the final cost models. The best model achieved an average absolute percentage error of 3.67% with 87% of the validation predictions falling within an error range of ±5%. The models are now being deployed for use within the operations of the industry partner to provide real feedback for model improvement.

Start Date Sep 2, 2013
Publication Date Dec 31, 2013
Publisher ARCOM Association of Researchers in Construction Management
Pages 559-568
ISBN 9780955239076
Institution Citation AHIAGA-DAGBUI, D.D. and SMITH, S.D. 2013. My cost runneth over: data mining to reduce construction cost overruns. In Smith, S.D. and Ahiaga-Dagbui, D.D. (eds.) Proceedings of the 29th Association of Researchers in Construction Management (ARCOM) annual conference, 2-4 September 2013, Reading, UK. Reading: ARCOM [online], pages 559-568. Available from: http://www.arcom.ac.uk/...Ahiaga-Dagbui_Smith.pdf
Keywords Artificial neural network; Cost estimation; Cost overrun; Data mining; Decision support system
Publisher URL http://www.arcom.ac.uk/-docs/proceedings/ar2013-0559-0568_Ahiaga-Dagbui_Smith.pdf

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