Perceptions of fuzzy set theory in construction risk analysis

Over the last three decades, organizations have increasingly been taking account of risk and uncertainty in their decision-making processes. The traditional methodology for risk management involves risk identification, risk analysis and management response to risk. Common risk analysis techniques in the construction industry include sensitivity analysis, probability analysis, Monte Carlo simulation, beta analysis etc. In this regard, Monte-Carlo simulation has been a touchstone of simulation efficiency across contemporary literature on risk analysis. However, a number of concerns have been raised about the intractability and construct validity of Monte Carlo representations for construction risks. The use of Monte Carlo simulation is based on the premise that the uncertain or risk event can be defined by a known probability distribution function. Unfortunately, this is not the case as far as risk is concerned in construction projects. It is in situations such as these where precise probability density functions cannot be ascribed to uncertain events that fuzzy set analysis becomes helpful. This research investigates the perceptions of fuzzy set theory and its influence on the practice of construction risk analysis. This work is part of a larger study that aims to investigate the specific insights that fuzzy set theory could bring into the construction risk analysis discipline. Purposive sampling was employed in selection of participants for the investigation. Structured interviews were conducted with highly experienced construction professionals in the United Kingdom. The results of these studies reveal that fuzzy set theory can enhance reasoning in analysing construction risks. However, fuzzy set theory as a stand-alone mathematical tool is not sufficient to complete the risk analysis process. Furthermore, the study indicates the vulnerability, volatility and computational intensiveness of fuzzy set theory may discourage construction practitioners from embracing the principles and applications of fuzzy set theory.