Sergey A. Glushenko - Post-graduate student, Department of Information Systems and Applied Computer Science,
Faculty of Computer Technologies and Information Security, Rostov State Economic University (RINE).
Address: 69, Bolshaya Sadovaya street, Rostov-on-Don, 344002, Russian Federation.
E-mail: gs-gears@yandex.ru

Alexey I. Doljenko - Professor, Department of Information Systems and Applied Computer Science, Faculty of Computer Technologies and Information Security, Rostov State Economic University (RINE).
Address: 69, Bolshaya Sadovaya street, Rostov-on-Don, 344002, Russian Federation.
E-mail: doljenkoalex@gmail.com

      This paper substantiates the importance of risk analysis in implementation of an investment and construction project (ICP) and validates feasibility of fuzzy logic in risk assessment.  Application of fuzzy models enables to consider both quantitative and qualitative characteristics, as well as to represent fuzzy descriptions by using fuzzy sets and linguistic variables.
      A fuzzy production model (FPM) introduced contains 19 input linguistic variables characterizing risk factors, 14 output linguistic variables characterizing risks in different areas of the ICP. The model builds on a set of 14 rules and allows a linguistic analysis of risks, which may cause potential detriment to a project, as well as to identify risk priorities (extremely  high, high, medium, low, extremely low) that are essential for investment & construction project management. The FPM enables to remove restrictions on the number of considered input variables and to integrate both qualitative and quantitative approaches to risk assessment.
      A problem statement is formulated for risk management tools to support fuzzy models and expediency of a proprietary decision support system (DSS) for risk analysis is justified. Then this paper describes a process of fuzzy modeling of the set of rules by using ModelingFuzzySet DSS that has been developed. Mamdani algorithm-based risk assessment mechanism enables to quantify risk, to obtain a linguistic description of a risk and expert’s degree of confidence relating to risk occurrence. 
      The simulation results have been used by decision-makers to identify risk priorities and allowed to develop an effective action plan to mitigate the impact of the most dangerous threats faced by an investment & construction project.