基于GRU算法的盾构掘进参数预测——以成都地铁19号线为例

肖浩汉; 陈祖煜; 徐国鑫; 蒋宗全; 苏岩; 曹瑞琅; 刘诗洋

doi:10.11988/ckyyb.20210916

PDF(2295 KB)

raybet体育在线院报 ›› 2023, Vol. 40 ›› Issue (1) : 123-131. DOI: 10.11988/ckyyb.20210916

岩土工程

基于GRU算法的盾构掘进参数预测——以成都地铁19号线为例

肖浩汉¹, 陈祖煜¹, 徐国鑫², 蒋宗全³, 苏岩², 曹瑞琅¹, 刘诗洋⁴

作者信息 +

Prediction of Shield Tunneling Parameters Based on GRU Algorithm: A Case Study on Chengdu Metro Line 19

XIAO Hao-han¹, CHEN Zu-yu¹, XU Guo-xin², JIANG Zong-quan³, SU Yan², CAO Rui-lang¹, LIU Shi-yang⁴

Author information +

文章历史 +

摘要

刀盘扭矩和刀盘推力是保障盾构机正常掘进的关键参数,对其准确预测可有效指导设备运行。本项研究的数据来源于成都地铁19号线土压平衡(EPB)盾构机的掘进数据。深入剖析了EPB盾构掘进数据的特点,提出了一种包含数据分割、异常值处理、数据降噪和数据编译4个阶段的标准数据预处理算法。在Butterworth滤波器基础上,利用门控循环单元(GRU)建立了盾构掘进参数预测模型,基于RMSE和MAE指标综合评估预测模型的预测效果。结果表明:预测模型对不同地质条件下的刀盘扭矩和刀盘推力掘进参数均能实现良好预测;经过Butterworth滤波,预测模型的预测精度提高显著;砂岩地层中,预测模型对刀盘扭矩的预测误差最小,RMSE和MAE分别为4.91和3.86。基于GRU算法的掘进参数预测,可提高盾构机掘进状态的判断水平,利于施工参数优化调整。

Abstract

Cutterhead torque (T) and cutterhead thrust (F) are key parameters to ensure the normal tunneling of shield machine,and their accurate prediction can effectively guide equipment operation.The research datasets are collected from earth pressure balance (EPB) shield machine on line 19 of the Chengdu Metro.By analyzing the characteristics of EPB data,we develop a standard data preprocessing algorithm that includes data segmentation,outlier processing,data filtering and data compilation.Based on Butterworth filter,we establish the prediction model of EPB tunneling parameters by gated recurrent unit (GRU) algorithm,and then comprehensively assess the prediction effect of the model by RMSE and MAE.Results manifest that the proposed model can achieve good prediction for T and F under different geological conditions,and the prediction accuracy of the GRU model in fusion with Butterworth filter is better than that of the unfiltered model.In sandstone formation,the prediction error of the model for T is the smallest,and the RMSE and MAE are 4.91 and 3.86,respectively.The prediction of tunneling parameters based on GRU algorithm can significantly improve the judgment level of shield tunneling state,which is conducive to the optimization and adjustment of construction parameters.

导出引用

肖浩汉, 陈祖煜, 徐国鑫, 蒋宗全, 苏岩, 曹瑞琅, 刘诗洋. 基于GRU算法的盾构掘进参数预测——以成都地铁19号线为例[J]. raybet体育在线院报. 2023, 40(1): 123-131 https://doi.org/10.11988/ckyyb.20210916

XIAO Hao-han, CHEN Zu-yu, XU Guo-xin, JIANG Zong-quan, SU Yan, CAO Rui-lang, LIU Shi-yang. Prediction of Shield Tunneling Parameters Based on GRU Algorithm: A Case Study on Chengdu Metro Line 19[J]. Journal of Changjiang River Scientific Research Institute. 2023, 40(1): 123-131 https://doi.org/10.11988/ckyyb.20210916

中图分类号： U455.43

参考文献

[1] 王梦恕.中国盾构和掘进机隧道技术现状、存在的问题及发展思路[J].隧道建设,2014,34(3):179-187.
[2] GONG Q M,YIN L J,MA H S,et al.TBM Tunnelling under Adverse Geological Conditions:An Overview[J].Tunnelling and Underground Space Technology,2016,57:4-17.
[3] 林存刚,吴世明,张忠苗,等.粉砂地层泥水盾构刀盘脱困工程实例分析[J].岩石力学与工程学报,2013,32(增刊1):2897-2906.
[4] 王超,龚国芳,杨华勇,等.NSVR硬岩隧道掘进机刀盘扭矩预测分析[J].浙江大学学报 (工学版),2018,52(3):479-486.
[5] LIN S S,SHEN S L,ZHANG N,et al.Modelling the Performance of EPB Shield Tunnelling Using Machine and Deep Learning Algorithms[J].Geoscience Frontiers,2021,12(5):101177.
[6] XU C,LIU X L,WANG E Z,et al.Prediction of Tunnel Boring Machine Operating Parameters using Various Machine Learning Algorithms[J].Tunnelling and Underground Space Technology,2021,109:103699.
[7] 闫长斌,汪鹤健,杨继华,等.利用PLSR-DNN耦合模型预测TBM净掘进速率[J].岩土力学,2021,42(2):519-528.
[8] LI J H,LI P X,GUO D,et al.Advanced Prediction of Tunnel Boring Machine Performance Based on Big Data[J].Geoscience Frontiers,2021,12(1):331-338.
[9] WANG Q,XIE X Y,SHAHROUR I.Deep Learning Model for Shield Tunneling Advance Rate Prediction in Mixed Ground Condition Considering Past Operations[J].IEEE Access,2020,8:215310-215326.
[10]GAO M Y,ZHANG N,SHEN S L,et al.Real-time Dynamic Earth-pressure Regulation Model for Shield Tunneling by Integrating GRU Deep Learning Method with GA Optimization[J].IEEE Access,2020,8:64310-64323.
[11]QIN C J,SHI G,TAO J F,et al.Precise Cutterhead Torque Prediction for Shield Tunneling Machines Using a Novel Hybrid Deep Neural Network[J].Mechanical Systems and Signal Processing,2021,151:107386.
[12]SUN W,SHI M L,ZHANG C,et al.Dynamic Load Prediction of Tunnel Boring Machine (TBM) Based on Heterogeneous in-situ Data[J].Automation in Construction,2018,92:23-34.
[13]周小雄,龚秋明,殷丽君,等.基于BLSTM-AM模型的TBM稳定段掘进参数预测[J].岩石力学与工程学报,2020,39(增刊2):3505-3515.
[14]李超,李涛,李正,等.基于BP神经网络的复合地层盾构掘进参数预测与分析[J].土木工程学报,2017,50(增刊1):145-150.
[15]侯少康,刘耀儒,张凯.基于IPSO-BP混合模型的TBM掘进参数预测[J].岩石力学与工程学报,2020,39(8):1648-1657.
[16]李建斌,郑赢豪,荆留杰,等.基于岩体聚类分级的TBM掘进参数预测方法[J].岩石力学与工程学报,2020,39(增刊2):3326-3337.
[17]CHO K,VAN MERRIENBOER B,GULCEHRE C,et al.Learning Phrase Representations using RNN Encoder-decoder for Statistical Machine Translation[C]∥Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP),doi:10.3115/v1/D14-1179.
[18]ZHANG W G,LI H R,LI Y Q,et al.Application of Deep Learning Algorithms in Geotechnical Engineering:A Short Critical Review[J].Artificial Intelligence Review,2021,54:5633-5673.
[19]GAO X J,SHI M L,SONG X G,et al.Recurrent Neural Networks for Real-time Prediction of TBM Operating Parameters[J].Automation in Construction,2019,98:225-235.
[20]ZHANG P,WU H N,CHEN R P,et al.A Critical Evaluation of Machine Learning and Deep Learning in Shield-ground Interaction Prediction[J].Tunnelling and Underground Space Technology,2020,106:103593.
[21]MAHMOODZADEH A,MOHAMMADI M,DARAEI A,et al.Forecasting Maximum Surface Settlement Caused by Urban Tunneling[J].Automation in Construction,2020,120:103375.
[22]MAHMOODZADEH A,MOHAMMADI M,NOORI K M G,et al.Presenting the Best Prediction Model of Water Inflow into Drill and Blast Tunnels among Several Machine Learning Techniques[J].Automation in Construction,2021,127:103719.
[23]LIU Z B,LI L,FANG X L,et al.Hard-rock Tunnel Lithology Prediction with TBM Construction Big Data Using a Global-attention-mechanism-based LSTM Network[J].Automation in Construction,2021,125:103647.
[24]XIAO HH,XING B,WANG Y,et al.Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies[J].Applied Sciences,2021,11(21):10264.
[25]HYNDMAN R J,SHANG H L.Rainbow Plots,Bagplots,and Boxplots for Functional Data[J].Journal of Computational and Graphical Statistics,2010,19(1):29-45.
[26]RAMIREZ-GALLEGO S,KRAWCZYK B,GARCIA S,et al.A Survey on Data Preprocessing for Data Stream Mining:Current Status and Future Directions[J].Neurocomputing,2017,239:39-57.
[27]LIU H C,SHAH S,JIANG W.On-line Outlier Detection and Data Cleaning[J].Computers and Chemical Engineering,2004,28(9):1635-1647.
[28]AGUINIS H,GOTTFREDSON R K,JOO H,et al.Best-Practice Recommendations for Defining,Identifying,and Handling Outliers[J].Organizational Research Methods,2013,16(2):270-301.
[29]ZHOU C,XU HH,DING L Y,et al.Dynamic Prediction for Attitude and Position in Shield Tunneling:A Deep Learning Method[J].Automation in Construction,2019,105:102840.
[30]中国生,徐国元,赵建平.基于小波变换的爆破地震信号阈值去噪的应用研究[J].岩土工程学报,2005,27(9):1055-1059.
[31]石崇,白金州,于士彦,等.基于复数傅里叶分析的岩土颗粒细观特征识别与随机重构方法[J].岩土力学,2016,37(10):2780-2786.
[32]GUSTAFSSON F.Determining the Initial States in Forward-backward Filtering[J].IEEE Transactions on Signal Processing,1996,44(4):988-992.
[33]AHLGREN P,JARNEVING B,ROUSSEAU R.Requirements for a Cocitation Similarity Measure,with Special Reference to Pearson's Correlation Coefficient[J].Journal of the American Society for Information Science and Technology,2003,54(6):550-560.
[34]杨旸,谭忠盛,彭斌,等.富水圆砾地层土压平衡盾构掘进参数优化研究[J].土木工程学报,2017,50(增刊1):94-98.
[35]CHUNG J,GULCEHRE C,CHO K H,et al.Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[C]∥NIPS 2014 Deep Learning and Representation Learning Workshop,doi:10.48550/arXiv.1412.3555.
[36]JUNG M J,LEE H,TANI J.Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition[J].Neural Networks,2018,105:356-370.