|Table of Contents|

Research on Deflection Temperature Effect Separation in Beidou Monitoring of Long-span Cable-stayed Bridge(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2019年05期
Page:
71-79
Research Field:
Publishing date:

Info

Title:
Research on Deflection Temperature Effect Separation in Beidou Monitoring of Long-span Cable-stayed Bridge
Author(s):
TAN Dong-mei1;  NIE Shun1;  QU Wei-lian1;  LIU Xiao-fei1;  WU Hao2
(1. Hubei Key Laboratory of Roadway Bridge & Structure Engineering, Wuhan University of Technology, Wuhan 430070, Hubei, China; 2. College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, Hubei, China)
Keywords:
waveform continuation;  wavelet decomposition;  MEEMD;  temperature effect
PACS:
TU317
DOI:
-
Abstract:
Aiming at the separation of deflection temperature effect in Beidou monitoring of long-span cable-stayed bridges, the periodic characteristic of deflection data was used to carry out waveform continuation at the beginning and the end of deflection data respectively. The high-amplitude vehicle deflection was removed from the wavelet details after wavelet decomposition and the residual components were gotten. The pre-denoising deflection was obtained by reconstruction of the residual components and wavelet coefficients. The deflection date could be denoised precisely by wavelet decomposition of pre-denoising deflection. After denoising deflection, it could be decomposed by the modified ensemble empirical mode decomposition(MEEMD)because of the fine characteristic that suppressed the mode aliasing in the decomposition process. After MEEMD decomposition, the first half cycle of daily temperature difference and annual temperature difference effect was symmetrically replaced to obtain high precision daily temperature difference and annual temperature difference effect. Finally, the residual components of denoising deflection after eliminating high precision daily temperature difference and annual temperature difference effect were decomposed again by MEEMD. The trend part was long-term deflection, thus the gradual separation of the daily temperature difference effect, the annual temperature difference effect and the long-term deflection were realized. The results show that the denoising algorithms of waveform continuation + pre-denoising + wavelet decomposition is more accurate than the traditional single denoising algorithm. The temperature effect separation algorithm can achieve accurate separation of each periodic component of the deflection temperature effect and it is suitable for deflection temperature effect separation for Beidou monitoring of long-span cable-stayed bridge.

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Last Update: 2019-09-29