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On-line Access: 2023-11-13

Received: 2022-12-20

Revision Accepted: 2023-05-27

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Journal of Zhejiang University SCIENCE A 2023 Vol.24 No.11 P.960-977


Short-term tunnel-settlement prediction based on Bayesian wavelet: a probability analysis method

Author(s):  Yang DING, Xiaowei YE, Zhi DING, Gang WEI, Yunliang CUI, Zhen HAN, Tao JIN

Affiliation(s):  Key Laboratory of Safe Construction and Intelligent Maintenance for Urban Shield Tunnels of Zhejiang Province, Hangzhou City University, Hangzhou 310015, China; more

Corresponding email(s):   jintao@hzcu.edu.cn

Key Words:  Metro construction, Settlement probability prediction, Structural health monitoring (SHM), Wavelet denoising, Gaussian prior (GP), Bayesian emulator (BE)

Yang DING, Xiaowei YE, Zhi DING, Gang WEI, Yunliang CUI, Zhen HAN, Tao JIN. Short-term tunnel-settlement prediction based on Bayesian wavelet: a probability analysis method[J]. Journal of Zhejiang University Science A, 2023, 24(11): 960-977.

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journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%T Short-term tunnel-settlement prediction based on Bayesian wavelet: a probability analysis method
%A Yang DING
%A Xiaowei YE
%A Gang WEI
%A Yunliang CUI
%A Zhen HAN
%A Tao JIN
%J Journal of Zhejiang University SCIENCE A
%V 24
%N 11
%P 960-977
%@ 1673-565X
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2200599

T1 - Short-term tunnel-settlement prediction based on Bayesian wavelet: a probability analysis method
A1 - Yang DING
A1 - Xiaowei YE
A1 - Zhi DING
A1 - Gang WEI
A1 - Yunliang CUI
A1 - Zhen HAN
A1 - Tao JIN
J0 - Journal of Zhejiang University Science A
VL - 24
IS - 11
SP - 960
EP - 977
%@ 1673-565X
Y1 - 2023
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2200599

As urbanization accelerates, the metro has become an important means of transportation. Considering the safety problems caused by metro construction, ground settlement needs to be monitored and predicted regularly, especially when a new metro line crosses an existing one. In this paper, we propose a settlement-probability prediction model with a bayesian emulator (BE) based on the gaussian prior (GP), that is, a GPBE. In addition, considering the distortion characteristics of monitoring data, the data is denoised using wavelet decomposition (WD), so the final prediction model is WD-GPBE. In particular, the effects of different prediction ratios and moving windows on prediction performance are explored, and the optimal number of moving windows is determined. In addition, the predicted value for GPBE based on the original data is compared with the predicted value for WD-GPBE based on the denoised data. One year of settlement-monitoring data collected by a structural health monitoring (SHM) system installed on the Nanjing Metro is used to demonstrate the effectiveness of WD-GPBE and GPBE for predicting settlement.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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