Risk Identification and Assessment of Mega Construction Projects in Uncertain Environments: A Gray Fuzzy Group Decision-Making and Monte Carlo Simulation Method

Mega construction projects (MCPs) face significant challenges in risk management due to their inherent complexity, particularly under uncertain environments. Existing risk management approaches often exhibit limitations when dealing with incomplete information and stakeholder divergence, making it difficult to comprehensively quantify risk uncertainty and its potential impacts. This study proposes a hybrid method that integrates gray fuzzy group decision-making (G-FGDM) with Monte Carlo simulation (MCS) to enhance risk identification and assessment in MCPs. First, a systematic framework of risk factors was developed through an extensive literature review. Second, the G-FGDM approach was employed to synthesize evaluations from multiple stakeholders, thereby quantifying the weights and fuzzy assessment values of various risk factors. Finally, MCS was used to simulate the probability distributions of different risk scenarios, enabling a dynamic analysis of the impact and uncertainty of risks on project success. Using a real-world MCP as a case study, the results indicate that technical and construction risks exert the highest impact, followed by financial and social risks, while policy risks demonstrate relatively stable effects. This study not only validates the effectiveness of the proposed hybrid method but also provides valuable decision-making support for improving risk management practices of MCPs in uncertain environments.