基于振动信号的盾构掘进地层辨识研究

王海涛; 周淳; 孙九春; 王悦

doi:10.3973/j.issn.2096-4498.2024.11.010

隧道建设 >

2024 , Vol. 44 >Issue 11: 2202 - 2212

DOI: https://doi.org/10.3973/j.issn.2096-4498.2024.11.010

研究与探索

基于振动信号的盾构掘进地层辨识研究

王海涛 ,
周淳 ,
孙九春 ,
王悦

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(1. 腾达建设集团股份有限公司，上海 200122; 2. 同济大学土木工程学院，上海 200092)

王海涛（1996—），男，安徽六安人，2021年毕业于上海工程技术大学，机械设计及理论专业，硕士,工程师，现从事地下工程施工关键技术问题研究及相关智能建造产品研发工作。E-mail: 18856216112@163.com。 *通信作者：孙九春， E-mail： sjczy999@163.com。

网络出版日期: 2024-12-12

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Stratum Identification in Shield Tunneling Based on Vibration Signals

WANG Haitao ,
ZHOU Chun ,
SUN Jiuchun ,
WANG Yue

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(1. Tengda Construction Group Co., Ltd., Shanghai 200122, China; 2. College of Civil Engineering, Tongji University, Shanghai 200092, China)

Online published: 2024-12-12

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摘要

针对泥水平衡盾构在掘进过程中难以实时辨识刀盘前方地质类型的问题，依托杭州之江路输水管廊及道路提升工程，通过在主驱动位置安装三轴加速度计，分析5类典型地层剖面信息与振动信号的关联性，以刀盘转动1圈的时间为周期，将原始信号划分为若干时间段，构建振动信号短时特征集，提出主成分分析(PCA)与支持向量机(SVM)相结合的刀盘前方地质类型实时辨识方法。结果表明： 1）地质类型对振动信号有着显著影响，沿Z轴方向（隧道前进方向）振动幅值最高，三轴E_RMS加速度中不同类地层条件下刀盘转矩和贯入度参数具有明显的聚类特性。2）PCA-SVM方法对工程沿线5类典型地质类型的识别准确率约97.98%；相比于采用原始加速度信号作为输入参数，采用分时间段提取的时域和频域特征作为输入参数的识别准确率提高23.36%。3）PCA算法能有效减少数据量，缩短模型训练时间约29.2%，提高识别模型准确率约0.3%。

关键词： 盾构掘进; 地层辨识; 振动信号; PCA-SVM

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王海涛 , 周淳 , 孙九春 , 王悦 . 基于振动信号的盾构掘进地层辨识研究[J]. 隧道建设, 2024 , 44(11) : 2202 -2212 . DOI: 10.3973/j.issn.2096-4498.2024.11.010

Abstract

Identifying the geological conditions ahead of the cutterhead during slurry balance shield tunneling in real time is challenging. To address this, a case study is conducted on the water supply pipeline and road improvement project at Zhijiang road in Hangzhou, China. Tri-axis accelerometers are installed on the shield′s main drive to collect vibration signals. The correlation between these signals and the five typical stratum profiles is examined. The original signals are divided into several times segments, with one rotation of the cutterhead as a cycle, thereby constructing a feature set. Finally, a method combining principal component analysis (PCA) and support vector machine (SVM) is proposed to identify geological types in real time ahead of the cutterhead. The results reveal the following: (1) Geological types considerably affect vibration signals, with the highest vibration amplitude along the Z-axis (tunneling direction). The root mean square acceleration across the three axes shows distinct clustering characteristics in cutterhead torque and penetration parameters under different stratum conditions. (2) The PCA-SVM method achieves an identification accuracy of 97.98% for these five typical stratum types along the project. The stratum identification accuracy using time and frequency domain features extracted by time periods as input parameters is 23.36% higher than that using the original acceleration signals as input parameters. (3) The PCA algorithm effectively reduces data volume, shortens model training time by approximately 29.2%, and improves identification accuracy by approximately 0.3%.

Key words： shield tunneling; stratum identification; vibration signal; principal component analysis-support vector machine method

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