Abstract: The big data storage and transmission brought by highfrequency sampling under multinode sensor networks in operating tunnel health monitoring systems are of great significance. Hence, the framework representation model of a sensor network is introduced to the data acquisition and transmission layer of operating tunnel structural health monitoring system, the traditional compressed sampling data reconstruction algorithm is improved, the minimum norm problem is solved by an iterative method, and the compressed sensing algorithm based on sensor network is proposed. The proposed method is verified by an engineering example. The results show the following. (1) Under the condition of a certain sampling rate, the compression reconstruction algorithm based on the sensor network can effectively increase the efficiency of data recovery, and the higher the sampling rate of the acceleration signal, the higher the reconstruction accuracy. (2) Under the condition of the same sampling rate, the accuracy of the reconstructed data obtained from the compression reconstruction algorithm based on the sensor network is higher than that of the traditional single nodecompression perception reconstruction algorithm. (3) When the compressionsampling rate is above 60%, the signal reconstruction error is less than 20%, which conforms to the engineering application standard. (4) The reconstruction algorithm based on sensor networks has limitations: there is an upper coupling limit for the number of sensor nodes, and when the number of sensor nodes exceeds 6, the precision of reconstructed signals does not significantly change.

Key words: operating tunnel, structural health monitoring, big data, compression sensing