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.
Abstract. This article utilizes gene expression programming (GEP) technique to develop a prediction model in order to automate estimating the construction cost of water and sewer replacement/rehabilitation projects. A database gathered for developing the model was established on the basis of data related to 210 actual water and sewer projects obtained from the City of San Diego, California, USA. To verify the predictability of the GEP model, it was examined to estimate the cost of the projects that were not included in the modelling process. Sensitivity analysis technique and professional experiences were employed to determine the contributions of the qualitative factors and quantifiable parameters affecting the cost estimate. The proposed model with correlation coefficient of 0.8467 is adequately capable of estimating the cost of water and sewer replacement/rehabilitation projects. The GEP-based design equation can easily be used for predesign purposes to help allocate budgets and available limited resources effectively.
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