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On-line Access: 2023-11-13

Received: 2022-12-20

Revision Accepted: 2023-05-27

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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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

Reference

[1]ChenRP, ZhangP, WuHN, et al., 2019. Prediction of shield tunneling-induced ground settlement using machine learning techniques. Frontiers of Structural and Civil Engineering, 13(6):1363-1378.

[2]ChengY, YeXF, FujiyamaT, 2020. Identifying crowding impact on departure time choice of commuters in urban rail transit. Journal of Advanced Transportation, 2020:8850565.

[3]ChitsazanN, NadiriAA, TsaiFTC, 2015. Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging. Journal of Hydrology, 528:52-62.

[4]DingLY, MaL, LuoHB, et al., 2011. Wavelet analysis for tunneling-induced ground settlement based on a stochastic model. Tunnelling and Underground Space Technology, 26(5):619-628.

[5]DingY, YeXW, GuoY, 2023a. Data set from wind, temperature, humidity and cable acceleration monitoring of the Jiashao bridge. Journal of Civil Structural Health Monitoring, 13(2-3):579-589.

[6]DingY, HangD, WeiYJ, et al., 2023b. Settlement prediction of existing metro induced by new metro construction with machine learning based on SHM data: a comparative study. Journal of Civil Structural Health Monitoring, in press.

[7]DingY, YeXW, GuoY, 2023c. A multistep direct and indirect strategy for predicting wind direction based on the EMD-LSTM model. Structural Control and Health Monitoring, 2023:4950487.

[8]DingY, YeXW, GuoY, et al., 2023d. Probabilistic method for wind speed prediction and statistics distribution inference based on SHM data-driven. Probabilistic Engineering Mechanics, 73:103475.

[9]DingY, YeXW, GuoY, 2023e. Copula-based JPDF of wind speed, wind direction, wind angle, and temperature with SHM data. Probabilistic Engineering Mechanics, 73:103483.

[10]DingY, YeXW, GuoY, 2023f. Wind load assessment with the JPDF of wind speed and direction based on SHM data. Structures, 47:2074-2080.

[11]DingY, YeXW, SuYH, et al., 2023g. A framework of cable wire failure mode deduction based on Bayesian network. Structures, 57:104996.

[12]FarrarCR, ParkG, AllenDW, et al., 2006. Sensor network paradigms for structural health monitoring. Structural Control and Health Monitoring, 13(1):210-225.

[13]GómezJ, CasasJR, VillalbaS, 2020. Structural health monitoring with distributed optical fiber sensors of tunnel lining affected by nearby construction activity. Automation in Construction, 117:103261.

[14]GongWP, LuoZ, JuangCH, et al., 2014. Optimization of site exploration program for improved prediction of tunneling-induced ground settlement in clays. Computers and Geotechnics, 56:69-79.

[15]HashemiM, BeheshtiS, 2010. Adaptive noise variance estimation in BayesShrink. IEEE Signal Processing Letters, 17(1):12-15.

[16]HashemiM, BeheshtiS, 2014. Adaptive Bayesian denoising for general Gaussian distributed signals. IEEE Transactions on Signal Processing, 62(5):1147-1156.

[17]HeXH, FangJ, ScanlonA, et al., 2010. Wavelet-based nonstationary wind speed model in Dongting lake cable-stayed bridge. Engineering, 2(11):895-903.

[18]HuangSX, WangXP, LiCF, et al., 2019. Data decomposition method combining permutation entropy and spectral substitution with ensemble empirical mode decomposition. Measurement, 139:438-453.

[19]JiZW, WangB, DengSP, et al., 2014. Predicting dynamic deformation of retaining structure by LSSVR-based time series method. Neurocomputing, 137:165-172.

[20]JiangXM, MahadevanS, AdeliH, 2007. Bayesian wavelet packet denoising for structural system identification. Structural Control and Health Monitoring, 14(2):333-356.

[21]Jiangsu Provincial Department of Housing and Urban Rural Development, 2015. Technical Specification for Monitoring Measurement of Urban Rail Transit Engineering in Jiangsu Province, DGJ32/J 195‍‒2015. Jiangsu Provincial Department of Housing and Urban Rural Development, China(in Chinese).

[22]KongLH, WuZC, ChenGH, et al., 2020. Crowdsensing-based cross-operator switch in rail transit systems. IEEE Transactions on Communications, 68(12):7938-7947.

[23]LawYZ, SantoH, LimKY, et al., 2020. Deterministic wave prediction for unidirectional sea-states in real-time using artificial neural network. Ocean Engineering, 195:106722.

[24]LiSH, ZhangMJ, LiPF, 2021. Analytical solutions to ground settlement induced by ground loss and construction loadings during curved shield tunneling. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 22(4):296-313.

[25]LiX, LiuX, LiCZ, et al., 2019. Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement. Structural Health Monitoring, 18(3):715-724.

[26]LiangJX, TangXW, WangTQ, et al., 2022. Numerical analysis of the influence of a river on tunnelling-induced ground deformation in soft soil. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 23(7):564-578.

[27]LiuWF, WuZZ, LiCY, et al., 2022. Prediction of ground-borne vibration induced by a moving underground train based on excitation experiments. Journal of Sound and Vibration, 523:116728.

[28]MOHURD (Ministry of Housing and Urban-Rural Development of the People’s Republic of China), 2013. Technical Code for Protection Structures of Urban Rail Transit, CJJ/T 202‒2013. MOHURD, China(in Chinese).

[29]MuBG, XieXK, LiX, et al., 2021. Monitoring, modelling and prediction of segmental lining deformation and ground settlement of an EPB tunnel in different soils. Tunnelling and Underground Space Technology, 113:103870.

[30]NgCWW, LiuGB, LiQ, 2013. Investigation of the long-term tunnel settlement mechanisms of the first metro line in Shanghai. Canadian Geotechnical Journal, 50(6):674-684.

[31]NiYQ, WangYW, ZhangC, 2020. A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data. Engineering Structures, 212:110520.

[32]QuHF, WangLH, FengCL, et al., 2021. Study on deformation and stability of rock-like materials retaining structure during collaborative construction of super-adjacent underground project. Advances in Materials Science and Engineering, 2021:5558544.

[33]QuK, XuYY, HuangJX, et al., 2023. Numerical simulation of hydrodynamic characteristics of submerged floating tunnels under the action of focused waves. Journal of Changsha University of Science & Technology (Natural Science), (04):127-141 (in Chinese).

[34]SamuiP, 2008. Support vector machine applied to settlement of shallow foundations on cohesionless soils. Computers and Geotechnics, 35(3):419-427.

[35]SandhamW, HamiltonD, FisherA, et al., 1998. Multiresolution wavelet decomposition of the seismocardiogram. IEEE Transactions on Signal Processing, 46(9):2541-2543.

[36]SendurL, SelesnickIW, 2002. Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Transactions on Signal Processing, 50(11):2744-2756.

[37]ShahinMA, JaksaMB, MaierHR, 2005. Neural network based stochastic design charts for settlement prediction. Canadian Geotechnical Journal, 42(1):110-120.

[38]SysynM, NabochenkoO, KovalchukV, 2020a. Experimental investigation of the dynamic behavior of railway track with sleeper voids. Railway Engineering Science, 28(3):290-304.

[39]SysynM, GerberU, KlugeF, et al., 2020b. Turnout remaining useful life prognosis by means of on-board inertial measurements on operational trains. International Journal of Rail Transportation, 8(4):347-369.

[40]SysynM, PrzybylowiczM, NabochenkoO, et al., 2021a. Identification of sleeper support conditions using mechanical model supported data-driven approach. Sensors, 21(11):3609.

[41]SysynM, PrzybylowiczM, NabochenkoO, et al., 2021b. Mechanism of sleeper–ballast dynamic impact and residual settlements accumulation in zones with unsupported sleepers. Sustainability, 13(14):7740.

[42]TayDB, 2021. Sensor network data denoising via recursive graph median filters. Signal Processing, 189:108302.

[43]WangCH, WangK, TangDF, et al., 2022. Spatial random fields-based Bayesian method for calibrating geotechnical parameters with ground surface settlements induced by shield tunneling. Acta Geotechnica, 17:1503-1519.

[44]WangF, GouBC, QinYW, 2013. Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine. Computers and Geotechnics, 54:125-132.

[45]WangMS, van der SchaarM, 2006. Operational rate-distortion modeling for wavelet video coders. IEEE Transactions on Signal Processing, 54(9):3505-3517.

[46]WuYQ, WangK, ZhangLZ, et al., 2018. Sand-layer collapse treatment: an engineering example from Qingdao Metro subway tunnel. Journal of Cleaner Production, 197:19-24.

[47]XiangYY, JiangZP, HeHJ, 2008. Assessment and control of metro-construction induced settlement of a pile-supported urban overpass. Tunnelling and Underground Space Technology, 23(3):300-307.

[48]YaoYP, QiSJ, CheLW, et al., 2018. Postconstruction settlement prediction of high embankment of silty clay at Chengde airport based on one-dimensional creep analytical method: case study. International Journal of Geomechanics, 18(7):05018004.

[49]YeXW, DingY, WanHP, 2019. Machine learning approaches for wind speed forecasting using long-term monitoring data: a comparative study. Smart Structures and Systems, 24(6):733-744.

[50]YeXW, DingY, WanHP, 2020. Statistical evaluation of wind properties based on long-term monitoring data. Journal of Civil Structural Health Monitoring, 10(5):987-1000.

[51]YeXW, DingY, WanHP, 2021. Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data. Structural Control and Health Monitoring, 28(1):e2650.

[52]ZhangLM, WuXG, JiWY, et al., 2017. Intelligent approach to estimation of tunnel-induced ground settlement using wavelet packet and support vector machines. Journal of Computing in Civil Engineering, 31(2):04016053.

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