RISK ANALYSIS OF BOT CONTRACTS USING SOFT COMPUTING

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.