The geological conditions of subsea tunnels are extremely complex, and a water inrush disaster is prone to occur when crossing a fault. Based on a geological analysis using cloud theory, analytic hierarchy process (AHP), and entropy weight theory, we aim to establish a normal cloud model with combination weight for water inrush risk evaluation in subsea tunnels. We select seven main factors, namely, the surrounding rock grade, rock integrity, overburden thickness, seawater thickness, tunnel section, permeability coefficient, and construction technology level as risk assessment indicators for the evaluation model. The risk is divided into five levels, and the numerical characters of the cloud model are calculated based on the standard of each risk level. The subjective weight and the objective weight are determined using AHP and the entropy weight method. The combination weight is calculated using the Delphi method. The comprehensive certainty degree is used to determine the risk level of water inrush. The model is applied to the Qingdao subsea tunnel to evaluate the water inrush risk crossing the F_4-1 fault, and the results are basically consistent with the actual excavation conditions. This article provides a new idea for the risk assessment of water inrush in the subsea tunnel.

Scientific site selection can significantly reduce construction risk and cost during construction of underground petroleum storage. Based on the analytic hierarchy process (AHP) theory and ideal point theory, a new site selection model for large underground petroleum storage is established in this paper. Nine indicators are selected as evaluation indices, including joint development, natural water-sealed condition, rock mass strength, in situ stress, environmental ecological vulnerability, regional stability, technical and economic conditions, topography, and meteorological conditions. Based on AHP theory, the weight of each evaluation index is determined. Secondly, the positive and negative ideal solutions of these evaluation indices are determined using ideal point theory. The ideal point evaluation function of each evaluation index is built to calculate the degree of closeness for the evaluation samples. Finally, the accuracy of the model is verified by using the model to evaluate the first underground petroleum storage project in China. The results show that the first six indicators are main controlling factors, and the sum of their weight is 88.3%. The result of the evaluation site level is good (II), which is suitable for construction. Moreover, the feasibility and accuracy of the evaluation criteria, and evaluation method is verified after cooperation with the power coefficient method and expert evaluation method. The research results can provide some references to the site selection of similar key projects.