Optimized Fuzzy Clustering Method for Health Monitoring of Shield Tunnels

Since health monitoring of shield tunnels generally employs multiple sensors belonging to different types,a fine analysis on massive monitoring data,as well as further quantitative health grading,is really challenging.An optimized fuzzy clustering analysis method based on the fuzzy equivalence relation is proposed for health monitoring of shield tunnels.Clustering results are auto-generated by using fuzzy similarity-valued map.The results follow the idea of unsupervised classification.Moreover,a convenient new health index HI is proposed for a fast tunnel-health grading.A case study on Nanjing Yangtze River Tunnel is presented to validate this method.Three types of indicators,namely soil pressure,pore water pressure and steel strain,are used to develop the clustering model.The clustering results are verified by analyzing the engineering geological conditions;the validity and the efficacy of the proposed method are also demonstrated.Further,the fuzzy clustering analysis also represents a potential for identifying abnormal monitoring data.This investigation indicates the fuzzy clustering analysis method is capable of characterizing the fuzziness of tunnel health,and beneficial to clarify the tunnel health evaluation uncertainties.