Dealing with construction cost overruns using data mining.

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

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 models 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.

Citation

AHIAGA-DAGBUI, D.D. and SMITH, S.D., 2014. Dealing with construction cost overruns using data mining. Construction management and economics [online], 32(7-8), pages 682-694. Available from: https://doi.org/10.1080/01446193.2014.933854

• Journal Article Type: Article
• Acceptance Date: Jun 9, 2014
• Online Publication Date: Jul 24, 2014
• Publication Date: Aug 31, 2014
• Deposit Date: Sep 24, 2015
• Publicly Available Date: Sep 24, 2015
• Journal: Construction management and economics
• Print ISSN: 0144-6193
• Electronic ISSN: 1466-433X
• Publisher: Taylor & Francis
• Peer Reviewed: Peer Reviewed
• Volume: 32
• Issue: 7-8
• Pages: 682-694
• DOI: https://doi.org/10.1080/01446193.2014.933854
• Keywords: Artificial neural networks; Bootstrapping; Cost overrun; Data mining; Ensemble modelling
• Public URL: http://hdl.handle.net/10059/1307