The big data storage and transmission brought by highfrequency
sampling under multinode 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 nodecompression perception reconstruction algorithm. (3) When the
compressionsampling 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.
WU Xianguo
,
DENG Tingting
,
CHEN Bin
,
ZENG Tiemei
,
CHEN Hongyu
,
ZHANG Kainan
. Research on Compressed Sensing of Big Data for
Structural Health Monitoring System of Operating
Tunnel[J]. Tunnel Construction, 2021
, 41(4)
: 674
-683
.
DOI: 10.3973/j.issn.2096-4498.2021.04.019
