Dominic D. Ahiaga-Dagbui
Dealing with construction cost overruns using data mining.
Ahiaga-Dagbui, Dominic D.; Smith, Simon D.
Authors
Simon D. Smith
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 |
Files
AHIAGA-DAGBUI 2014 Dealing with construction cost
(412 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Exploring visual asset management collaboration: learning from the oil and gas sector.
(2017)
Conference Proceeding
Evaluating the whole-life cost implication of revocability and disruption in office retrofit building projects.
(2016)
Conference Proceeding
Spotlight on construction cost overrun research: superficial, replicative and stagnated.
(2015)
Conference Proceeding
Modelling economic risks in megaproject construction: a systemic approach.
(2015)
Conference Proceeding