大直径泥水盾构掘进参数智能预测

李健; 陶博文; 蔡琦; 姚建强; 王赶

doi:10.11988/ckyyb.20231316

PDF(7297 KB)

raybet体育在线院报 ›› 2025, Vol. 42 ›› Issue (3) : 148-155. DOI: 10.11988/ckyyb.20231316

工程安全与灾害防治

大直径泥水盾构掘进参数智能预测

李健 ¹ ,
陶博文 ¹ ,
蔡琦 ¹ ,
姚建强 ² ,
王赶 ²

作者信息 +

Intelligent Prediction of Tunneling Parameters for Large Diameter Slurry Shield

LI Jian ¹ ,
TAO Bo-wen ¹ ,
CAI Qi ¹ ,
YAO Jian-qiang ² ,
WANG Gan ²

Author information +

文章历史 +

摘要

依托北京市东六环改造项目中京哈高速—潞苑北大街标段大直径泥水盾构隧道工程,结合地应力提取与掘进断面地层信息编码的地质数据处理方法,建立考虑地质条件的盾构参数智能预测模型,提出包含数据获取、数据预处理、数据分解、智能预测模型构建、模型训练与测试、结果评价与分析的一套智能预测分析方法,并对依托工程的盾构掘进参数进行了预测分析。分析结果表明在考虑地质条件后,盾构推力与刀盘扭矩的预测精度分别提高了38.53%与44.86%,保证了后续盾构掘进的施工安全。

Abstract

An intelligent prediction model for shield tunneling parameters accounting for geological conditions is presented by employing a geological data processing technique which integrates in-situ stress extraction and tunneling section stratum information coding. The method encompasses data acquisition, preprocessing and decomposition, and model construction, training and testing, as well as result evaluation and analysis. The model is applied to predict the shield parameters for the large-diameter slurry shield tunnel project of the Luyuan North Street section on the Beijing-Harbin Expressway within the Beijing East Sixth Ring Road reconstruction project. Findings reveal that accounting for geological conditions enhances the prediction accuracy of shield thrust and cutter-head torque by 38.53% and 44.86%, respectively. This improvement secures the construction safety of subsequent shield tunneling operations. The research outcomes can serve as a reference for future similar projects.

导出引用

李健, 陶博文, 蔡琦, 等. 大直径泥水盾构掘进参数智能预测[J]. raybet体育在线院报. 2025, 42(3): 148-155 https://doi.org/10.11988/ckyyb.20231316

LI Jian, TAO Bo-wen, CAI Qi, et al. Intelligent Prediction of Tunneling Parameters for Large Diameter Slurry Shield[J]. Journal of Changjiang River Scientific Research Institute. 2025, 42(3): 148-155 https://doi.org/10.11988/ckyyb.20231316

中图分类号： U455

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	管会生, 高波. 盾构刀盘扭矩估算的理论模型[J]. 西南交通大学学报, 2008, 43(2): 213-217, 226. (GUAN Hui-sheng, GAO Bo. Theoretical Model for Estimation of Cutter Head Torque in Shield Tunneling[J]. Journal of Southwest Jiaotong University, 2008, 43(2):213-217, 226. (in Chinese)) 本文引用 [2]

[2]

李潮, 周宏伟, 左建平, 等. 土压平衡盾构刀盘扭矩计算方法与多因素量化分析[J]. 岩石力学与工程学报, 2013, 32(4): 760-766.

(LI

Chao

, ZHOU

Hong-wei

, ZUO

Jian-ping

, et al. Torque Calculation Method of Cutterhead in Earth Pressure Balance Shield and Quantitative Analysis of Several Influencing Factors[J]. Chinese Journal of Rock Mechanics and Engineering, 2013, 32(4): 760-766. (in Chinese))

本文引用 [1]

[3]

魏新江, 王凡勇, 丁智, 等. 软土区盾构刀盘扭矩分析及对地表变形影响[J]. 中南大学学报(自然科学版), 2018, 49(6): 1491-1497.

(WEI

Xin-jiang

, WANG

Fan-yong

, DING

Zhi

, et al. Analysis of Torque of Shield Cutter Head and Its Influence on Surface Deformation in Soft Soil Region[J]. Journal of Central South University (Science and Technology), 2018, 49(6): 1491-1497. (in Chinese))

本文引用 [1]

[4]	GHASEMI E, YAGIZ S, ATAEI M. Predicting Penetration Rate of Hard Rock Tunnel Boring Machine Using Fuzzy Logic[J]. Bulletin of Engineering Geology and the Environment, 2014, 73(1): 23-35. 本文引用 [1]

[5]

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.

(ZHOU

Xiao-xiong

, GONG

Qiu-ming

, YIN

Li-jun

, et al. Predicting Boring Parameters of TBM Stable Stage Based on BLSTM Networks Combined with Attention Mechanism[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39 (Supp.2): 3505-3515. (in Chinese))

本文引用 [1]

[6]

肖浩汉, 陈祖煜, 徐国鑫, 等. 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

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。

(XIAO

Hao-han

, CHEN

Zu-yu

, XU

Guo-xin

, et al. Prediction of Shield Tunneling Parameters Based on GRU Algorithm: a Case Study on Chengdu Metro Line 19[J]. Journal of Yangtze River Scientific Research Institute, 2023, 40(1): 123-131. (in Chinese))

https://doi.org/10.11988/ckyyb.20210916

本文引用 [1] 摘要

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.

[7]	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. 本文引用 [1]

[8]

徐一帆, 王士民, 何川, 等. 基于BP神经网络的复合地层盾构掘进参数预测[J]. 铁道标准设计, 2022, 66(7): 120-125.

(XU

Yi-fan

, WANG

Shi-min

, HE

Chuan

, et al. Prediction of Driving Parameters of Shield Tunnel in Composite Strata Based on back Propagation Neural Network[J]. Railway Standard Design, 2022, 66(7): 120-125. (in Chinese))

本文引用 [2]

[9]

朱小藻. 复杂地层中盾构掘进速度的调控分析:以新建铁路横琴至珠海机场段HJZQ-2标隧道工程为例[J]. 隧道建设(中英文), 2020, 40(增刊1):107-114.

(ZHU

Xiao-zao

. Analysis of EPB Shield Advancing Speed Control in Composite Strata: A Case Study on Tunnel Project of HJZQ-2 Bid of Newly-built Hengqin-Zhuhai Airport Section[J]. Tunnel Construction, 2020, 40 (Supp.1): 107-114. (in Chinese))

本文引用 [1]

[10]	TIAN G, MA H. Application of Multidimensional Association Mining in Shield Tunneling Optimization[J]. Geotechnical and Geological Engineering, 2019, 37(3): 1869-1876. 本文引用 [1]

[11]

张莹, 蔡宗熙, 冷永刚, 等. 盾构机掘进参数的关联分析与地质特征识别[J]. 哈尔滨工程大学学报, 2011, 32(4): 476-480.

(ZHANG

Ying

, CAI

Zong-xi

, LENG

Yong-gang

, et al. Correlative Analysis of Shield Tunneling Data and Recognition of Geologic Features[J]. Journal of Harbin Engineering University, 2011, 32(4): 476-480. (in Chinese))

本文引用 [1]

[12]	SUN W, SHI M, 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. 本文引用 [1]

[13]	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. 本文引用 [1]

[14]	RAMÍREZ-GALLEGO S, KRAWCZYK B, GARCÍA S, et al. A Survey on Data Preprocessing for Data Stream Mining: Current Status and Future Directions[J]. Neurocomputing, 2017, 239: 39-57. 本文引用 [1]

[15]	LIU H, SHAH S, JIANG W. On-line Outlier Detection and Data Cleaning[J]. Computers & Chemical Engineering, 2004, 28(9): 1635-1647. 本文引用 [1]

[16]	AGUINIS H, GOTTFREDSON R K, JOO H. Best-practice Recommendations for Defining, Identifying, and Handling Outliers[J]. Organizational Research Methods, 2013, 16(2): 270-301. 本文引用 [1]

[17]	WANG G, FANG Q, DU J, et al. Deep Learning-based Prediction of Steady Surface Settlement Due to Shield Tunnelling[J]. Automation in Construction, 2023, 154:105006. 本文引用 [1]

[18]	ZEILER M D, FERGUS R. Visualizing and Understanding Convolutional Networks[C]// Proceedings of the 13th European Conference: Computer Vision - ECCV 2014. Cham: Springer International Publishing. Zurich, Switzerland, September 6-12, 2014: 818-833. 本文引用 [1]

[19]	YIN J, DENG Z, INES A V M, et al. Forecast of Short-term Daily Reference Evapotranspiration under Limited Meteorological Variables Using a Hybrid Bi-directional Long Short-term Memory Model (Bi-LSTM)[J]. Agricultural Water Management, 2020, 242: 106386. 本文引用 [1]

[20]

T A

, ARKHIPOV

M Y

, BURTSEV

M S

. Application of a Hybrid Bi-LSTM-CRF Model to the Task of Russian Named Entity Recognition[C]// Proceedings of the 7th International Conference:Artificial Intelligence and Natural Language. Cham: Springer International Publishing. St. Petersburg, Russia, October 17-19, 2018: 91-103.

本文引用 [1]