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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.
Additional information
Notes on contributors
Neda Shahrara
Neda SHAHRARA. She received her PhD from Eastern Mediterranean University, North Cyprus. She is a Civil Engineer at the City of San Diego, California, USA. Her research interests include Public Private Partnership (PPP), investment appraisal, project cost estimation and life cycle costing.
Tahir Çelik
Tahir ÇELIK. He is a professor in the Civil Engineering Department at Cyprus International University, North Cyprus. He is the founder and the director of Construction Engineering and Management Program. His research interests include quality management, life cycle costing, estimating construction projects, construction planning and construction techniques.
Amir H. Gandomi
Amir H. GANDOMI. He received his PhD from University of Akron, OH. He was selected as an elite in 2008 by National Elites Foundation. He used to be a lecturer in several universities, and he is currently a distinguished research fellow in an NSF center for the study of evolution in action (BEACON) located at Michigan State University, MI. Dr Gandomi has published over one hundred journal papers and four books. Some of those publications are now among the hottest papers in the field, and collectively have been cited more than 5,000 times (h-index = 37). He also served as associate editor, editor and guest editor in several prestigious journals. His research interests are evolutionary computations and their applications in (big) data mining and optimization of engineering systems.