Risk Assessment of Underground Tunnel Engineering Based on Pythagorean Fuzzy Sets and Bayesian Networks

With the acceleration of urbanization, the importance of risk management in underground construction projects has become increasingly prominent. In the process of risk assessment for underground construction projects, the uncertainty of subjective factors from experts poses a significant challenge to the accuracy of assessment outcomes. This paper takes a section of the Nanchang Metro Line 2 as the research object, aiming to address the subjectivity issues in the risk assessment of underground construction projects and to enhance the scientific rigor and accuracy of the assessment. The study initially conducts a comprehensive identification and analysis of risk factors in underground engineering through a literature review and expert consultation method. Based on this, this paper introduces the theory of Pythagorean fuzzy sets to improve the Delphi method in order to reduce the impact of subjectivity in expert assessments. Furthermore, this paper constructs a Bayesian network model, incorporating risk factors into the network, and quantifies the construction risks through a probabilistic inference mechanism. The research findings indicate a total of 12 key risk factors that have been identified across four dimensions: geological and groundwater conditions, tunnel construction technical risks, construction management measures, and the surrounding environment. The Bayesian network assessment results indicate that the effectiveness of engineering quality management and the state of safety management at the construction site are the two most influential factors. Based on the assessment results, this paper further conducts a risk control analysis and proposes targeted risk management measures.