CLC number:
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2024-01-04
Cited: 0
Clicked: 1008
Long RAN, Yang DING, Qizhi CHEN, Baoping ZOU, Xiaowei YE. Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction[J]. Journal of Zhejiang University Science A, 2023, 24(12): 1106-1119.
@article{title="Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction",
author="Long RAN, Yang DING, Qizhi CHEN, Baoping ZOU, Xiaowei YE",
journal="Journal of Zhejiang University Science A",
volume="24",
number="12",
pages="1106-1119",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2200573"
}
%0 Journal Article
%T Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction
%A Long RAN
%A Yang DING
%A Qizhi CHEN
%A Baoping ZOU
%A Xiaowei YE
%J Journal of Zhejiang University SCIENCE A
%V 24
%N 12
%P 1106-1119
%@ 1673-565X
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2200573
TY - JOUR
T1 - Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction
A1 - Long RAN
A1 - Yang DING
A1 - Qizhi CHEN
A1 - Baoping ZOU
A1 - Xiaowei YE
J0 - Journal of Zhejiang University Science A
VL - 24
IS - 12
SP - 1106
EP - 1119
%@ 1673-565X
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2200573
Abstract: Urban subway tunnel construction inevitably disturbs the surrounding rock and causes the deformation of existing subway structures. Dynamic predictions of the tunnel horizontal displacement, tunnel ballast settlement, and tunnel differential settlement are important for ensuring the safety of buildings and tunnels. First, based on the Hangzhou Metro project, we analyzed the influence of construction on the deformation of existing subway structures and the difficulties and key points in monitoring. Then, a deformation prediction model, based on a back propagation (BP) neural network, was established with massive monitoring data. In particular, we analyzed the influence of four structures of the BP neural network on prediction performance, i.e., single input–single hidden layer–single output, multiple inputs–single hidden layer–single output, single input–double hidden layers–single output, and multiple inputs–double hidden layers–single output, and verified them using measured data.
[1]ChenRP, ZhangP, KangX, et al., 2019a. Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods. Soils and Foundations, 59(2):284-295.
[2]ChenRP, ZhangP, WuHN, et al., 2019b. Prediction of shield tunneling-induced ground settlement using machine learning techniques. Frontiers of Structural and Civil Engineering, 13(6):1363-1378.
[3]ChenRP, SongX, MengFY, et al., 2022. Analytical approach to predict tunneling-induced subsurface settlement in sand considering soil arching effect. Computers and Geotechnics, 141:104492.
[4]DengHS, FuHL, YueS, et al., 2022. Ground loss model for analyzing shield tunneling-induced surface settlement along curve sections. Tunnelling and Underground Space Technology, 119:104250.
[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, DingZ, et al., 2023f. Short-term tunnel-settlement prediction based on Bayesian wavelet: a probability analysis method. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 24(11):960-977.
[11]DingY, YeXW, GuoY, 2023g. Wind load assessment with the JPDF of wind speed and direction based on SHM data. Structures, 47:2074-2080.
[12]DingY, YeXW, SuYH, et al., 2023h. A framework of cable wire failure mode deduction based on Bayesian network. Structures, 57:104996.
[13]DingZ, ZhangMB, ZhangX, et al., 2023. Theoretical analysis on the deformation of existing tunnel caused by under-crossing of large-diameter slurry shield considering construction factors. Tunnelling and Underground Space Technology, 133:104913.
[14]FangY, CuiJ, WanatowskiD, et al., 2022. Subsurface settlements of shield tunneling predicted by 2D and 3D constitutive models considering non-coaxiality and soil anisotropy: a case study. Canadian Geotechnical Journal, 59(3):424-440.
[15]FangYS, WuCT, ChenSF, et al., 2014. An estimation of subsurface settlement due to shield tunneling. Tunnelling and Underground Space Technology, 44:121-129.
[16]FengLY, ZhangLM, 2022. Enhanced prediction intervals of tunnel-induced settlement using the genetic algorithm and neural network. Reliability Engineering & System Safety, 223:108439.
[17]GuoJ, ZhengJJ, LiuY, 2009. Application of an immune algorithm to settlement prediction. Journal of Zhejiang University-SCIENCE A, 10(1):93-100.
[18]HasanipanahM, Noorian-BidgoliM, Jahed ArmaghaniD, et al., 2016. Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Engineering with Computers, 32(4):705-715.
[19]JinDL, YuanDJ, LiXG, et al., 2018. Analysis of the settlement of an existing tunnel induced by shield tunneling underneath. Tunnelling and Underground Space Technology, 81:209-220.
[20]KimD, PhamK, OhJY, et al., 2022. Classification of surface settlement levels induced by TBM driving in urban areas using random forest with data-driven feature selection. Automation in Construction, 135:104109.
[21]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.
[22]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.
[23]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.
[24]LiuCY, WangY, HuXM, et al., 2021. Application of GA-BP neural network optimized by grey Verhulst model around settlement prediction of foundation pit. Geofluids, 2021:5595277.
[25]LiuLN, ZhouW, GutierrezM, 2022. Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets. Journal of Rock Mechanics and Geotechnical Engineering, 14(4):1028-1041.
[26]LuDC, LinQT, TianY, et al., 2020. Formula for predicting ground settlement induced by tunnelling based on Gaussian function. Tunnelling and Underground Space Technology, 103:103443.
[27]LuY, WangYY, LiY, 2023. Passenger flow forecast of urban bus stops based on deep learning. Journal of Changsha University of Science & Technology (Natural Science), in press (in Chinese).
[28]MOHURD (Ministry of Housing and Urban-Rural Development of the People’s Republic of China), 2013. Code for Monitoring Measurement of Urban Rail Transit Engineering, GB50911-2013. MOHURD, China(in Chinese).
[29]Phien-WejN, GiaoPH, NutalayaP, 2006. Land subsidence in Bangkok, Thailand. Engineering Geology, 82(4):187-201.
[30]PourtaghiA, Lotfollahi-YaghinMA, 2012. Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunneling. Tunnelling and Underground Space Technology, 28:257-271.
[31]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), 20(4):127-141 (in Chinese).
[32]SuwansawatS, EinsteinHH, 2006. Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunnelling and Underground Space Technology, 21(2):133-150.
[33]TashayoB, BehzadafsharK, Soltani TehraniM, et al., 2019. Feasibility of imperialist competitive algorithm to predict the surface settlement induced by tunneling. Engineering with Computers, 35(3):917-923.
[34]WangJB, WangXP, ZhangQ, et al., 2021. Dynamic prediction model for surface settlement of horizontal salt rock energy storage. Energy, 235:121421.
[35]WangJB, ZhouPY, SongZP, et al., 2022. A new calculation method for tunneling-caused stratum settlement. KSCE Journal of Civil Engineering, 26(6):2624-2640.
[36]WuSS, ZhaoGF, WuBS, 2022. Real-time prediction of the mechanical behavior of suction caisson during installation process using GA-BP neural network. Engineering Applications of Artificial Intelligence, 116:105475.
[37]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.
[38]YeXW, DingY, WanHP, 2021. Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data. Structural Control and Health Monitoring, 28(1):e2650.
[39]YeXW, JinT, ChenYM, 2022. Machine learning-based forecasting of soil settlement induced by shield tunneling construction. Tunnelling and Underground Space Technology, 124:104452.
[40]YuHL, LiDB, GaoW, et al., 2023. Analysis of tunnel detection based on geological radar and laser scanning. Journal of Changsha University of Science & Technology (Natural Science), 20(3):102-117 (in Chinese).
[41]ZhangDM, ZhangJZ, HuangHW, et al., 2020. Machine learning-based prediction of soil compression modulus with application of 1D settlement. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 21(6):430-444.
[42]ZhangK, LyuHM, ShenSL, et al., 2020. Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements. Tunnelling and Underground Space Technology, 106:103594.
[43]ZhangN, ZhangN, ZhengQ, et al., 2022. Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network. Acta Geotechnica, 17(4):1167-1182.
[44]ZhangP, WuHN, ChenRP, et al., 2020. Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: a comparative study. Tunnelling and Underground Space Technology, 99:103383.
[45]ZhengG, SunJB, ZhangTQ, et al., 2023. Settlement of a pile under cyclic lateral loads in dry sand. Géotechnique, 73(7):561-571.
[46]ZhuCH, LiN, 2017. Prediction and analysis of surface settlement due to shield tunneling for Xi’an Metro. Canadian Geotechnical Journal, 54(4):529-546.
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