Risk and Uncertainty Assessment Model in Construction Projects Using Fuzzy Logic

Abstract

In the global construction market, most construction companies are willing to undertake international projects in order to maximize their profitability by taking advantage of attractive emerging markets and minimize dependence on local market. Due to the nature of construction works, there are lots of risks and uncertainties associated with the company and project conditions. Therefore, how the profitability of the project changes with occurrence of various risk events, in other words, the sensitivity of project costs to risk events, should be estimated realistically. This paper offers a comprehensive risk assessment methodology that provides a decision support tool, which can be utilized through the bidding decisions for international construction projects introducing a risk model that facilitate this assessment procedure, prioritize these projects and evaluate risk contingency value. The risk models is developed using the analytic hierarchy process (AHP) to evaluate risk factors weights (likelihood) and FUZZY LOGIC approach to evaluate risk factors impact (Risk consequences) using software aids such as EXCEL and MATLAB software. The reliability of the developed software has been tested by applications on a real construction projects. The proposed methodology and decision support tool have been proved to be reliable for the estimation of cost overrun due to risk while giving bidding decisions in international markets. Therefore, the developed model can be used to sort projects based upon risk, which facilitate company's decision of which project can be pursued. The developed risk model is validated, which prove its robustness in risk assessment (97%) in company level and (105%) in project level. It can also be used to sort construction projects based upon its risks. The developed contingency risk model demonstrates the ability to evaluate risk contingency value by aggregating rules combining company risk index and project risk index using fuzzy logic approach and MATLAB software.