Title: Deformation detection algorithm of shallow and large-span tunnel support structure based on wireless sensor network

Authors: Huawei Wu; Chuan Sun; Yicheng Li; Yong Kuang

Addresses: Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle; School of Automotive and Traffic Engineering, Hubei University of Arts and Science, Xiangyang 441053, China ' School of Mechanical, Electronic and Automotive Engineering, Huanggang Normal University, Huanggang 438000, China; The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China ' Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China ' Dongfeng Xiangyang Touring Car Co., Ltd, Hubei, China

Abstract: In order to improve the safety of tunnel support structure, a deformation detection algorithm based on wireless sensor network is proposed. The wireless sensor network nodes were deployed with the randomly distributed autonomous network grid structure, and the deformation force parameters of the support structures of shallow span and large span tunnels were obtained with the pressure sensor, and the power spectrum characteristic quantities of the sensor parameters were extracted. The time-frequency characteristic decomposition method is used to realise the beamforming processing deformation parameters. The quantitative analysis ability of the improved structure deformation is detected according to the detection of deformation amplitude and the estimation of related parameters. The experimental results show that the detection accuracy of the method is improved from 87% to 100%, and the detection performance is improved, which verifies the effectiveness of the method.

Keywords: wireless sensor network; shallow and large-span tunnel; support structure; deformation detection.

DOI: 10.1504/IJIPT.2020.110313

International Journal of Internet Protocol Technology, 2020 Vol.13 No.4, pp.219 - 227

Received: 08 Nov 2018
Accepted: 02 Jun 2019
Published online: 14 Oct 2020 *

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