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.

The rock classification system was initially applied to drill-and-blast tunnels and subsequently adapted for TBM tunnels; however, the majority of these systems primarily focused on rock stability while neglecting considerations of boreability. Compared with conventional tunnels, TBM tunnels are characterized by their rapid tunneling speed and excellent self-stabilization of the rock mass. Therefore, it is imperative to develop a novel rock mass classification system that considers both the tunneling efficiency of TBMs and the required support measures for tunnel construction. This paper introduces a novel rock classification system for TBM tunnels that accurately predicts the construction rate by evaluating the penetration rate and daily utilization, enabling a more precise assessment of daily advancement in tunneling. Firstly, the penetration rate and construction utilization in different rock strata are analyzed based on comprehensive statistics of existing construction data. Consequently, a discriminant matrix for classifying the boreability of rock is derived. Subsequently, employing the Ensemble Classifier method, a regression prediction model for rock boreability classification is established by incorporating input parameters such as thrust, torque, rotational speed, field penetration index, and the uniaxial compressive strength of rock. The validity of the proposed model is verified by comparing predicted machine performance with actual data sets. The proposed method presents a novel approach for predicting the performance of TBM construction.

Changes in hydrogeological conditions directly affect the utility of water curtain systems and the safety of oil storage caverns during the construction and operation of underground water-sealed oil storage caverns. Therefore, hydrogeological conditions are of great importance in surrounding rock classification. This study is based on an actual project. The PCA-extenics model is applied to evaluate the exact hydrogeologic grade of the oil storage cavern. Then, the authors introduce rough set theory to calculate the weight of each influential factor. Finally, the research establishes the RS-extenics model for surrounding rock prediction based on the weights and evaluates the reliability of the model. The results suggest that the RQD has the most significant weight (0.3158) and is crucial to surrounding rock classification. The prediction results are generally consistent with the actual values. The results indicate that the model has good engineering applicability and value. The study provides a reference for the prediction of hydrogeological grades and classification of rock surrounding oil storage caverns.