基于K-means-云模型的输水隧洞安全评价方法及应用OA

With the rapid development of water diversion and transfer projects in China,water conveyance tunnels,as core hydraulic structures,play a pivotal role in achieving the overall benefits of the projects.However,due to the long distances and complex geological conditions leading to significant sectional differences along these tunnels,existing safety evaluation methods face challenges of inadequate adaptability and weak integration capabilities for monitoring data,making it difficult to accurately assess tunnel safety conditions.This paper proposes a novel safety evaluation method integrating K-means clustering and the cloud model:tunnel zoning is achieved using the K-means clustering method;a combination weighting method is employed to balance subjective and objective weights;and a safety evaluation system is constructed based on the cloud model.Taking a long-distance inter-basin water supply project as a case study,deformation monitoring data from a 1,350-meter section of the main tunnel were analyzed.The K-means algorithm partitioned the tunnel into five distinct zones(clusters).Cloud model evaluation indicated that four zones(cluster 1,etc.)were in a"normal"state,while cluster 3 was"basically normal".The comprehensive evaluation concluded that the entire tunnel is currently in a"basically normal"state,aligning with its actual operational condition.This method,combining zonal evaluation with fuzzy processing,enhances assessment accuracy,provides a scientific basis for tunnel safety monitoring,and contributes to the safe and efficient operation of water diversion and transfer projects.