Economics at your fingertips  

Dealing with construction cost overruns using data mining

Dominic Ahiaga-Dagbui and Simon D. Smith

Construction Management and Economics, 2014, vol. 32, issue 7-8, 682-694

Abstract: One of the main aims of any construction client is to procure a project within the limits of a predefined budget. However, most construction projects routinely overrun their cost estimates. Existing theories on construction cost overrun suggest a number of causes ranging from technical difficulties, optimism bias, managerial incompetence and strategic misrepresentation. However, much of the budgetary decision-making process in the early stages of a project is carried out in an environment of high uncertainty with little available information for accurate estimation. Using non-parametric bootstrapping and ensemble modelling in artificial neural networks, final project cost-forecasting models were developed with 1600 completed projects. This helped to extract information embedded in data on completed construction projects, in an attempt to address the problem of the dearth of information in the early stages of a project. It was found that 92% of the 100 validation predictions were within ±10% of the actual final cost of the project while 77% were within ±5% of actual final cost. This indicates the model's ability to generalize satisfactorily when validated with new data. The models are being deployed within the operations of the industry partner involved in this research to help increase the reliability and accuracy of initial cost estimates.

Date: 2014
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed

Downloads: (external link) (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1080/01446193.2014.933854

Access Statistics for this article

Construction Management and Economics is currently edited by Will Hughes

More articles in Construction Management and Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

Page updated 2021-07-17
Handle: RePEc:taf:conmgt:v:32:y:2014:i:7-8:p:682-694