Risk analysis of BOT contracts using soft computing

Shahrara, N; Çelik, T; Gandomi, AH

Risk analysis of BOT contracts using soft computing

Shahrara, N Çelik, T Gandomi, AH

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Publisher:: Vilnius Gediminas Technical University Press
Publication Type:: Journal Article
Citation:: Journal of Civil Engineering and Management, 2017, 23, (2), pp. 232-240
Issue Date:: 2017-02-17

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Field	Value	Language
dc.contributor.author	Shahrara, N
dc.contributor.author	Çelik, T
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2022-08-25T03:08:24Z
dc.date.available	2022-08-25T03:08:24Z
dc.date.issued	2017-02-17
dc.identifier.citation	Journal of Civil Engineering and Management, 2017, 23, (2), pp. 232-240
dc.identifier.issn	1392-3730
dc.identifier.issn	1822-3605
dc.identifier.uri	http://hdl.handle.net/10453/160825
dc.description.abstract	Build-Operate-Transfer (BOT) contracts have been widely implemented in developing countries facing budget constraints. Analysing the expected variability in project viability requires extensive risk analysis. An objective analysis of various risk variables and their influence on a BOT project evaluation requires study and integration of many scenarios into the concession terms, which is complicated and time-consuming. If the process of negotiating the financial parameters and uncertainties of a BOT project could be automated, this would be a milestone in objective decision-making from various stakeholders’ points of view. A soft computing model would let the user incorporate as many scenarios as could be provided. Extensive risk analysis could then be easily performed, leading to more accurate and dependable results. In this research, an artificial neural network model with correlation coefficient of 0.9064 has been used to model the relationship between important project parameters and risk variables. This information was extracted from sensitivity analysis and Monte Carlo simulation results obtained from conventional spreadsheet data. The resulting consensus would yield to fair contractual agreements for both the government and the concession company.
dc.language	English
dc.publisher	Vilnius Gediminas Technical University Press
dc.relation.ispartof	Journal of Civil Engineering and Management
dc.relation.isbasedon	10.3846/13923730.2015.1068844
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 1202 Building
dc.subject.classification	Civil Engineering
dc.title	Risk analysis of BOT contracts using soft computing
dc.type	Journal Article
utslib.citation.volume	23
utslib.for	0905 Civil Engineering
utslib.for	1202 Building
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-25T03:08:23Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	2

Abstract:

Build-Operate-Transfer (BOT) contracts have been widely implemented in developing countries facing budget constraints. Analysing the expected variability in project viability requires extensive risk analysis. An objective analysis of various risk variables and their influence on a BOT project evaluation requires study and integration of many scenarios into the concession terms, which is complicated and time-consuming. If the process of negotiating the financial parameters and uncertainties of a BOT project could be automated, this would be a milestone in objective decision-making from various stakeholders’ points of view. A soft computing model would let the user incorporate as many scenarios as could be provided. Extensive risk analysis could then be easily performed, leading to more accurate and dependable results. In this research, an artificial neural network model with correlation coefficient of 0.9064 has been used to model the relationship between important project parameters and risk variables. This information was extracted from sensitivity analysis and Monte Carlo simulation results obtained from conventional spreadsheet data. The resulting consensus would yield to fair contractual agreements for both the government and the concession company.

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http://hdl.handle.net/10453/160825