A Risk-Based Cost Contingency Estimation Model for Infrastructure Projects

Allahaim, FS; Liu, L; Kong, X

A Risk-Based Cost Contingency Estimation Model for Infrastructure Projects

Allahaim, FS Liu, L Kong, X

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Publisher:: American Society of Civil Engineers (ASCE)
Publication Type:: Conference Proceeding
Citation:: ICCREM 2016: BIM Application and Offsite Construction - Proceedings of the 2016 International Conference on Construction and Real Estate Management, 2016, pp. 1293-1303
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Allahaim, FS
dc.contributor.author	Liu, L
dc.contributor.author	Kong, X https://orcid.org/0000-0001-7855-858X
dc.date.accessioned	2023-01-29T23:28:09Z
dc.date.available	2023-01-29T23:28:09Z
dc.date.issued	2016-01-01
dc.identifier.citation	ICCREM 2016: BIM Application and Offsite Construction - Proceedings of the 2016 International Conference on Construction and Real Estate Management, 2016, pp. 1293-1303
dc.identifier.isbn	9780784480274
dc.identifier.uri	http://hdl.handle.net/10453/165560
dc.description.abstract	Cost overrun of infrastructure projects is widespread and represents significant financial risks to stakeholders. The cluster analysis on the data of 41 commonly cited causes identified four dimensions of causes: scope changes, market and regulatory uncertainty, inadequate planning and control, and unforeseen circumstances. Subsequently, these four dimensions were then used to develop a risk-based cost contingency estimation model (RBCCEM) to improve the accuracy of cost contingency estimation which was then validated using a bootstrapping approach. The accuracy of the cost estimation measures was used to compare RBCCEM with other methods, namely fixed cost contingency, reference class forecasting, and a hybrid method. The findings suggested that RBCCEM could be more accurate as the error decreased by 10%. As a result, RBCCEM produces contingency estimates that are more consistent and brings project costs closer to actual costs than alternative approaches, such as reference class forecasting (RCF).
dc.language	en
dc.publisher	American Society of Civil Engineers (ASCE)
dc.relation.ispartof	ICCREM 2016: BIM Application and Offsite Construction - Proceedings of the 2016 International Conference on Construction and Real Estate Management
dc.relation.ispartof	ICCREM 2016
dc.relation.isbasedon	10.1061/9780784480274.160
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Risk-Based Cost Contingency Estimation Model for Infrastructure Projects
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-01-29T23:28:08Z
pubs.publication-status	Published

Abstract:

Cost overrun of infrastructure projects is widespread and represents significant financial risks to stakeholders. The cluster analysis on the data of 41 commonly cited causes identified four dimensions of causes: scope changes, market and regulatory uncertainty, inadequate planning and control, and unforeseen circumstances. Subsequently, these four dimensions were then used to develop a risk-based cost contingency estimation model (RBCCEM) to improve the accuracy of cost contingency estimation which was then validated using a bootstrapping approach. The accuracy of the cost estimation measures was used to compare RBCCEM with other methods, namely fixed cost contingency, reference class forecasting, and a hybrid method. The findings suggested that RBCCEM could be more accurate as the error decreased by 10%. As a result, RBCCEM produces contingency estimates that are more consistent and brings project costs closer to actual costs than alternative approaches, such as reference class forecasting (RCF).

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http://hdl.handle.net/10453/165560