Full Text:   <632>

Summary:  <118>

CLC number: 

On-line Access: 2024-01-15

Received: 2023-01-07

Revision Accepted: 2023-08-07

Crosschecked: 2024-01-15

Cited: 0

Clicked: 877

Citations:  Bibtex RefMan EndNote GB/T7714


Xiaowei YE


-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2024 Vol.25 No.1 P.1-17


Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm

Author(s):  Xiaowei YE, Xiaolong ZHANG, Yanbo CHEN, Yujun WEI, Yang DING

Affiliation(s):  MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   chenyanbo@zju.edu.cn

Key Words:  Random forest (RF), Particle swarm optimization (PSO), Upward displacement of lining, Machine learning prediction, Shield tunneling construction

Share this article to: More |Next Article >>>

Xiaowei YE, Xiaolong ZHANG, Yanbo CHEN, Yujun WEI, Yang DING. Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm[J]. Journal of Zhejiang University Science A, 2024, 25(1): 1-17.

@article{title="Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm",
author="Xiaowei YE, Xiaolong ZHANG, Yanbo CHEN, Yujun WEI, Yang DING",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm
%A Xiaowei YE
%A Xiaolong ZHANG
%A Yanbo CHEN
%A Yujun WEI
%A Yang DING
%J Journal of Zhejiang University SCIENCE A
%V 25
%N 1
%P 1-17
%@ 1673-565X
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2300011

T1 - Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm
A1 - Xiaowei YE
A1 - Xiaolong ZHANG
A1 - Yanbo CHEN
A1 - Yujun WEI
A1 - Yang DING
J0 - Journal of Zhejiang University Science A
VL - 25
IS - 1
SP - 1
EP - 17
%@ 1673-565X
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2300011

During construction, the shield linings of tunnels often face the problem of local or overall upward movement after leaving the shield tail in soft soil areas or during some large diameter shield projects. Differential floating will increase the initial stress on the segments and bolts which is harmful to the service performance of the tunnel. In this study we used a random forest (RF) algorithm combined particle swarm optimization (PSO) and 5-fold cross-validation (5-fold CV) to predict the maximum upward displacement of tunnel linings induced by shield tunnel excavation. The mechanism and factors causing upward movement of the tunnel lining are comprehensively summarized. Twelve input variables were selected according to results from analysis of influencing factors. The prediction performance of two models, PSO-RF and RF (default) were compared. The Gini value was obtained to represent the relative importance of the influencing factors to the upward displacement of linings. The PSO-RF model successfully predicted the maximum upward displacement of the tunnel linings with a low error (mean absolute error (MAE)=4.04 mm, root mean square error (RMSE)=5.67 mm) and high correlation (R2=0.915). The thrust and depth of the tunnel were the most important factors in the prediction model influencing the upward displacement of the tunnel linings.


结论:1.提出的PSO-RF混合模型明显提升了管片最大上浮量预测模型的预测性能;2. PSO-RF管片上浮预测模型成功预测了管片的最大上浮量,有更小的预测误差(MAE=4.04mm,RMSE=5.67mm)与更高的相关性(R2=0.915);3.盾构机千斤顶推力与隧道埋深是影响管片最大上浮量预测模型性能的主要因素。


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


[1]BreimanL, 2001. Random forests. Machine Learning, 45(1):5-32.

[2]ChenRP, LiJ, KongLG, et al., 2013. Experimental study on face instability of shield tunnel in sand. Tunnelling and Underground Space Technology, 33:12-21.

[3]ChenRP, LiuY, LiuSX, et al., 2014. Characteristics of upward moving for lining during shield tunnelling construction. Journal of Zhejiang University (Engineering Science), 48(6):1068-1074 (in Chinese).

[4]ChenRP, ZhangP, KangX, et al., 2019. Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods. Soils and Foundations, 59(2):284-295.

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

[6]DingY, YeXW, GuoY, 2023b. 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.

[7]DingY, HangD, WeiYJ, et al., 2023c. 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.

[8]ElbazK, ShenSL, SunWJ, et al., 2020. Prediction model of shield performance during tunneling via incorporating improved particle swarm optimization into ANFIS. IEEE Access, 8:39659-39671.

[9]ElbazK, YanT, ZhouAN, et al., 2022. Deep learning analysis for energy consumption of shield tunneling machine drive system. Tunnelling and Underground Space Technology, 123:104405.

[10]ElbazK, ZhouAN, ShenSL, 2023. Deep reinforcement learning approach to optimize the driving performance of shield tunnelling machines. Tunnelling and Underground Space Technology, 136:105104.

[11]El-GalladA, El-HawaryM, SallamA, et al., 2002. Enhancing the particle swarm optimizer via proper parameters selection. Canadian Conference on Electrical and Computer Engineering, Conference Proceedings, 2:792-797.

[12]FargnoliV, GragnanoCG, BoldiniD, et al., 2015. 3D numerical modelling of soil‍–‍structure interaction during EPB tunnelling. Géotechnique, 65(1):23-37.

[13]GengDX, HuYC, JiangYL, et al., 2021. Modified calculation model for segment floating in slurry shield tunnel. Journal of Performance of Constructed Facilities, 35(5):04021068.

[14]HoTK, 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):832-844.

[15]KohestaniVR, BazarganlariMR, MarnaniJA, 2017. Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest. Journal of AI and Data Mining, 5(1):127-135.

[16]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.

[17]LinSS, ShenSL, ZhouAN, 2022. Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network. Journal of Rock Mechanics and Geotechnical Engineering, 14(4):1232-1240.

[18]LovattiBPO, NascimentoMHC, NetoÁC, et al., 2019. Use of random forest in the identification of important variables. Microchemical Journal, 145:1129-1134.

[19]LuoWP, YuanDJ, JinDL, et al., 2020. Prediction and analysis of slurry pressure at the shield cut in composite strata based on random forest. China Civil Engineering Journal, 53(S1):43-49 (in Chinese).

[20]LvF, YuJ, ZhangJ, et al., 2022. A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation. Journal of Zhejiang University-SCIENCE A (Applied Physics and Engineering), 23(12):1027-1046.

[21]LvQQ, ZhouJJ, YangZX, et al., 2017. Prediction of shield tunnel segment up-floating caused by formation rebound. Tunnel Construction, 37(S2):87-93 (in Chinese).

[22]MengXH, BabaeeH, KarniadakisGE, 2021. Multi-fidelity Bayesian neural networks: algorithms and applications. Journal of Computational Physics, 438:110361.

[23]NeuGE, EdlerP, FreitagS, et al., 2022. Reliability based optimization of steel-fibre segmental tunnel linings subjected to thrust jack loadings. Engineering Structures, 254:113752.

[24]RaissiM, PerdikarisP, KarniadakisGE, 2019. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686-707.

[25]ShanF, HeXZ, ArmaghaniDJ, et al., 2022. Success and challenges in predicting TBM penetration rate using recurrent neural networks. Tunnelling and Underground Space Technology, 130:104728.

[26]ShenSL, ElbazK, ShabanWM, et al., 2022. Real-time prediction of shield moving trajectory during tunnelling. Acta Geotechnica, 17(4):1533-1549.

[27]ShiJW, ChenYH, LuH, et al., 2022. Centrifuge modeling of the influence of joint stiffness on pipeline response to underneath tunnel excavation. Canadian Geotechnical Journal, 59(9):1568-1586.

[28]ShuY, ZhouSH, JiC, et al., 2017. Analysis of shield tunnel segment uplift data and uplift value forecast during tunnel construction in variable composite formation. Chinese Journal of Rock Mechanics and Engineering, 36(S1):3464-3474 (in Chinese).

[29]TalmonAM, BezuijenA, 2013. Analytical model for the beam action of a tunnel lining during construction. International Journal for Numerical and Analytical Methods in Geomechanics, 37(2):181-200.

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

[31]WangJ, FengK, WangYC, et al., 2022. Soil disturbance induced by EPB shield tunnelling in multilayered ground with soft sand lying on hard rock: a model test and DEM study. Tunnelling and Underground Space Technology, 130:104738.

[32]WangSM, HeC, NieL, et al., 2019. Study on the long-term performance of cement-sodium silicate grout and its impact on segment lining structure in synchronous backfill grouting of shield tunnels. Tunnelling and Underground Space Technology, 92:103015.

[33]WangSM, LinZY, PengXY, et al., 2022. Research and evaluation on water-dispersion resistance of synchronous grouting slurry in shield tunnel. Tunnelling and Underground Space Technology, 129:104679.

[34]XuYF, SunDA, SunJ, et al., 2003. Soil disturbance of Shanghai silty clay during EPB tunnelling. Tunnelling and Underground Space Technology, 18(5):537-545.

[35]YangL, MengXH, KarniadakisGE, 2021. B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data. Journal of Computational Physics, 425:109913.

[36]YangQ, GengP, WangJX, et al., 2022. Research of asphalt–cement materials used for shield tunnel backfill grouting and effect on anti-seismic performance of tunnels. Construction and Building Materials, 318:125866.

[37]YeF, ZhuHH, DingWQ, 2008. Longitudinal upward movement analysis of shield tunnel based on elastic foundation beam. China Railway Science, 29(4):65-69 (in Chinese).

[38]YeJN, LiuY, ChenRP, et al., 2014. Study of the permissible value of upward floating for segment in shield tunnel construction. Chinese Journal of Rock Mechanics and Engineering, 33(s2):4067-4074 (in Chinese).

[39]YeXW, JinT, YunCB, 2019. A review on deep learning-based structural health monitoring of civil infrastructures. Smart Structures and Systems, 24(5):567-585.

[40]YeXW, JinT, ChenYM, 2022. Machine learning-based forecasting of soil settlement induced by shield tunneling construction. Tunnelling and Underground Space Technology, 124:104452.

[41]YeXW, ZhangXL, ZhangHQ, et al., 2023. Prediction of lining upward movement during shield tunneling using machine learning algorithms and field monitoring data. Transportation Geotechnics, 41:101002.

[42]ZhangP, ChenRP, WuHN, 2019. Real-time analysis and regulation of EPB shield steering using random forest. Automation in Construction, 106:102860.

[43]ZhangP, YinZY, JinYF, 2022a. Bayesian neural network-based uncertainty modelling: application to soil compressibility and undrained shear strength prediction. Canadian Geotechnical Journal, 59(4):546-557.

[44]ZhangP, YinZY, JinYF, et al., 2022b. Physics-informed multifidelity residual neural networks for hydromechanical modeling of granular soils and foundation considering internal erosion. Journal of Engineering Mechanics, 148(4):04022015.

[45]ZhouJ, LiXB, MitriHS, 2016. Classification of rockburst in underground projects: comparison of ten supervised learning methods. Journal of Computing in Civil Engineering, 30(5):04016003.

[46]ZhouJ, ShiXZ, DuK, et al., 2017. Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel. International Journal of Geomechanics, 17(6):04016129.

[47]ZhouSH, JiC, 2014. Tunnel segment uplift model of earth pressure balance shield in soft soils during subway tunnel construction. International Journal of Rail Transportation, 2(4):221-238.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE