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

conference contribution
posted on 2013-01-01, 00:00 authored by Dominic Doe Ahiaga-DagbuiDominic Doe Ahiaga-Dagbui, S D Smith
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



Association of Researchers in Construction Management. Annual Conference (29th : 2013 : Reading, United Kingdom)


Association of Researchers in Construction Management Annual Conference


559 - 568


ARCOM Association of Researchers in Construction Management


Reading, U.K.

Place of publication

Reading, U.K.

Start date


End date




Publication classification

E Conference publication; E1.1 Full written paper - refereed

Copyright notice

2013, ARCOM


S Smith, D Ahiaga-Dagbui

Title of proceedings

ARCOM 2013 : Proceedings of the 29th Annual Conference of Association of Researchers in Construction Management

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