Deformation data analysis of shield tunnel based on ICEEMDAN- wavelet -NLMS joint model

Zexu Yin; Yufeng Shi

doi:10.1117/12.3035435

10 July 2024 Deformation data analysis of shield tunnel based on ICEEMDAN- wavelet -NLMS joint model

Zexu Yin, Yufeng Shi

Author Affiliations +

Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 132232G (2024) https://doi.org/10.1117/12.3035435
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

The settlement deformation of subway tunnels during the construction and operation stages is a relatively complex nonlinear process. The original settlement deformation monitoring data will be affected by some noise during the collection process. In order to reduce the impact of noise and errors in the subsequent prediction process, a joint denoising model based on the improved empirical mode decomposition (ICEEMDAN) algorithm, wavelet threshold denoising and NLMS adaptive filtering was constructed. The model first uses ICEEMDAN to decompose the original data. After decomposing the intrinsic modal component IMF, it divides it into high-frequency and low-frequency components. Then the wavelet threshold is used to remove the components with high correlation coefficients in the high-frequency and low-frequency components. Noise and NLMS adaptive filtering are processed, and finally the additive reconstruction is performed to obtain the denoised data. Experimental analysis results show that compared with ICEEMDAN, wavelet threshold denoising and NLMS adaptive filtering algorithms, the joint denoising model has better denoising effect, higher correlation coefficient, signal-to-noise ratio and smaller mean square root error.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zexu Yin and Yufeng Shi "Deformation data analysis of shield tunnel based on ICEEMDAN- wavelet -NLMS joint model", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 132232G (10 July 2024); https://doi.org/10.1117/12.3035435

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Denoising

Wavelets

Tunable filters

Deformation

Data modeling

Correlation coefficients

Signal to noise ratio

Show All Keywords

Keywords/Phrases

Search In:

Publication Years