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On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2023-11-14

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yang DING

https://orcid.org/0000-0002-1298-1710

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

http://doi.org/10.1631/jzus.A2200599


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",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2200599"
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Abstract: 
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.

基于小波-贝叶斯的隧道短期沉降预测:一种概率分析方法

作者:丁杨1,2,3,叶肖伟4,丁智1,魏纲1,崔允亮1,韩震5,金涛1
机构:1浙大城市学院,土木工程学系,中国杭州,310015;2浙大城市学院,城市基础设施智能化浙江省工程研究中心,中国杭州,310015;3浙大城市学院,浙江省城市盾构隧道安全建造与智能养护重点实验室,中国杭州,310015;4浙江大学,建筑工程学院,中国杭州,310058;5南京地铁运营有限责任公司,中国南京,210012
目的:隧道沉降是会严重影响隧道结构及其临近建筑的安全隐患。本文旨在建立一种沉降预测模型用于实时预测南京地铁隧道的沉降情况,并通过探讨沉降数据的预处理方法和所提模型中的网络结构(移动窗口和预测比例)对预测性能的影响,确定模型的最优结构组成。
创新点:1.考虑未知沉降值的不确定性,并结合高斯先验和协方差函数推导出沉降预测值的均值和方差表达式;2.基于现场实测数据,验证所提出模型的有效性。
方法:1.通过理论推导,构建考虑沉降不确定性的贝叶斯预测模型,并结合高斯先验过程计算得到沉降预测值的均值和方差表达式;2.通过南京地铁现场实测数据验证所提模型的有效性,并通过参数敏感性分析,确定最优移动窗口及预测比例;3.通过数值计算,探讨数据预处理方法对模型预测精度的影响。
结论:1.本文提出的概率预测模型能够预测沉降的发展规律,且沉降变化值均在95%的置信区间内。2.移动窗口过大会导致概率预测模型过拟合,而移动窗口过小则会导致概率预测模型欠拟合;针对本文的沉降数据,最佳移动窗口为5。3.对原始沉降数据进行小波降噪处理,能够提高概率预测模型的预测精度。

关键词:地铁建设;沉降概率预测;结构健康监测;小波去噪;高斯先验;贝叶斯仿真

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

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