A Fuzzy-Based Risk Assessment Methodology for Construction Projects Under Epistemic Uncertainty

In this paper, a methodology for construction projects risk assessment under epistemic uncertainty (i.e., uncertainty arising from lack of data/knowledge) has been proposed. In practice, as the sufficient data from historical sources for probabilistic analysis are quite difficult to obtain, qualitative risk assessment methodologies based on expert's judgments are commonly used in construction industry. However, these insufficient probabilistic data combined with experts' judgments can be used in the risks evaluation process to reduce uncertainties and biasness. All the methodologies developed so far have assumed that the degrees of uncertainties (i.e., levels of uncertainties) involved in individual risk event are equal. However, in practice, the degree of uncertainties that involved in each risk event may vary due to the variation in the availability or quality of data obtained from multiple sources (e.g., from experts' opinions and past data from similar projects). Therefore, evaluation of risks considering the degree of uncertainty involved in individual risk events may assist project manager in setting-up response strategies to mitigate threat to the project objectives. This paper proposes a risk assessment methodology using triangular fuzzy numbering system to compute risk value by combining expert's opinion and insufficient historical data. A modified form of general ramp-type fuzzy membership function for quantification of uncertainty range of each risk event and an extended VIKOR method for risk ranking with these uncertainty ranges have been used. The most notable difference with other fuzzy risk assessment methods is the use of algorithm to handle the uncertainties involved in individual risk event. The proposed risk assessment methodology is illustrated for two practical example problems: (1) a steel-frame structured building and (2) a rehabilitation project of a building.