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Fast vibration characteristics analysis of an underwater shield tunnel using the accelerometer network enhanced by edge computing.

Authors :
Li, Chen-dong
Zhang, Wei
Zhu, Hong-Hu
Wang, Peng
Ren, Jia-Tao
Spencer, Billie F.
Source :
Measurement (02632241). Jul2019, Vol. 141, p52-61. 10p.
Publication Year :
2019

Abstract

• The vibration edge computing architecture of a shield tunnel was proposed. • A prototype vibration test enhanced by the edge computing was implemented. • The vibration characteristics can be viewed on mobile devices simultaneously. • The dominant frequency of vibration in the low frequency band is determined. Edge computing can reduce the latency induced by the long communication path from end users to the cloud center in the Internet of Things (IoT). In this paper, we implement a vibration test of an underwater shield tunnel using a wired accelerometer network enhanced by edge computing to verify a vibration edge computing architecture that we propose. The cloudlet for edge computing can temporarily store raw data and analyze them simultaneously. It can also serve as the hub of the accelerometer. The acquired acceleration data and their results can be viewed on mobile devices in real time from the cloudlet. The results of measurements show that the amplitude of vibrations of components of the tunnel decreased distinctly from the pavement lane to the segment lining and the base. Moreover, the dominant frequency of a low-frequency band was determined by the tunnel–soil interaction, and represents the overall vibration characteristics of the tunnel–soil system along the Z-axis. The quickly analyzed online results agreed well with those of an offline simulation in terms of general trends. This study highlights the potential of the proposed vibration edge computing architecture for underwater shield tunnels. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02632241
Volume :
141
Database :
Academic Search Index
Journal :
Measurement (02632241)
Publication Type :
Academic Journal
Accession number :
137110722
Full Text :
https://doi.org/10.1016/j.measurement.2019.03.053