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Article(id=1228279679774294142, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1228279664221815452, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2406374, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1724515200000, receivedDateStr=2024-08-25, revisedDate=1745856000000, revisedDateStr=2025-04-29, acceptedDate=null, acceptedDateStr=null, onlineDate=1770774295990, onlineDateStr=2026-02-11, pubDate=1754582400000, pubDateStr=2025-08-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1770774295990, onlineIssueDateStr=2026-02-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1770774295990, creator=13701087609, updateTime=1770774295990, updator=13701087609, issue=Issue{id=1228279664221815452, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='22', pageStart='9211', pageEnd='9648', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1770774292283, creator=13701087609, updateTime=1770777611996, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1228293588207992892, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1228279664221815452, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1228293588207992893, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1228279664221815452, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=9568, endPage=9577, ext={EN=ArticleExt(id=1228279680768344283, articleId=1228279679774294142, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Intelligent Prediction and Control Optimization Methods for Floating of Shield Tunnel Segments, columnId=1228279669053649867, journalTitle=Science Technology and Engineering, columnName=Papers·Traffics and Transportations, runingTitle=null, highlight=null, articleAbstract=

In order to meet the demand for segment floating prediction in shield construction and the problem of insufficient training data for deep learning models, a set of shield segment floating prediction model was proposed by combining the tunneling mechanism of the shield machine with the process of segment floating.The numerical simulation software was used to simulate the process of segment floating of the shield structure, and using the large amount of numerical simulation data and the engineering field data for the deep learning training, so as to realize the data enhancement of the segment floating prediction model. The prediction model consists of the tube sheet floating process. The prediction model consists of a segment floating prediction model and two auxiliary models, which consider the interaction of active control and passive response parameters. Finally, a typical case study was carried out based on the shield section of the Beijing East 6th Ring Road Rehabilitation Project, and the results show that the prediction accuracy of the model is controlled within 4 mm, which meets the project requirements. The grouting parameters of the shield tail have the greatest influence on the model performance, followed by the digging parameters, and the shield attitude parameters have the smallest influence. Moreover, the training data of the segment floating based on the numerical simulation data can improve the prediction accuracy of the prediction model by 30%, which proves the effectiveness of the data enhancement method. The effectiveness of the data enhancement method is demonstrated. The data enhancement method based on numerical simulation data proposed in the article provides a new idea for the training and optimization of similar deep learning models.

, correspAuthors=Gan WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yang WANG, Yi-sheng WANG, Qi CAI, Zhao-hui LIU, Gan WANG), CN=ArticleExt(id=1228279687537951419, articleId=1228279679774294142, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于数据增强的盾构隧道管片上浮智能预测模型, columnId=1228279670290969583, journalTitle=科学技术与工程, columnName=论文·交通运输, runingTitle=null, highlight=null, articleAbstract=

针对盾构工程中管片上浮预测的需求与深度学习模型训练数据不足的难题,结合盾构机掘进机制与管片上浮过程提出了一套盾构管片上浮预测模型;利用数值模拟软件模拟盾构管片上浮过程,将海量数值模拟数据与工程现场数据同时用于深度学习训练,实现针对管片上浮预测模型的数据增强。其中预测模型由管片上浮预测模型和两个辅助模型组成,考虑了主动控制和被动响应参数的相互作用。最后基于北京东六环入地改造工程盾构段展开典型事例研究。研究结果表明,模型预测精度控制在4 mm以内,满足工程需求;盾尾注浆参数对模型性能的影响最大,其次是掘进参数,而盾构姿态参数的影响最小;基于数值模拟数据的管片上浮训练数据能提升预测模型的预测精度30%,证明了该数据增强方法的有效性,本文提出的基于数值模拟数据的数据增强方法,为类似深度学习模型的训练与优化提供了新的思路。

, correspAuthors=王赶, authorNote=null, correspAuthorsNote=
* 王赶(1997—),男,汉族,安徽安庆人,博士后。研究方向:盾构隧道智能建造。E-mail:。
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汪洋(1991—),男,汉族,安徽芜湖人,硕士,高级工程师。研究方向:盾构隧道施工及智能建造技术。E-mail:。

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汪洋(1991—),男,汉族,安徽芜湖人,硕士,高级工程师。研究方向:盾构隧道施工及智能建造技术。E-mail:。

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汪洋(1991—),男,汉族,安徽芜湖人,硕士,高级工程师。研究方向:盾构隧道施工及智能建造技术。E-mail:。

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keyword=数据增强)], refs=[Reference(id=1228369870161642219, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, doi=null, pmid=null, pmcid=null, year=2021, volume=41, issue=2, pageStart=157, pageEnd=164, url=null, language=null, rfNumber=[1], rfOrder=0, authorNames=钱七虎, 陈健, journalName=隧道建设(中英文), refType=null, unstructuredReference=钱七虎, 陈健. 大直径盾构掘进风险分析及对特大直径盾构挑战的思考[J]. 隧道建设(中英文), 2021, 41(2): 157-164., articleTitle=大直径盾构掘进风险分析及对特大直径盾构挑战的思考, refAbstract=null), Reference(id=1228369870266499825, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, doi=null, pmid=null, pmcid=null, year=2021, volume=41, issue=2, pageStart=157, pageEnd=164, url=null, language=null, rfNumber=[1], rfOrder=1, authorNames=Qian Qihu, Chen Jian, journalName=Tunnel Construction, refType=null, unstructuredReference=Qian Qihu, Chen Jian. 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articleId=1228279679774294142, companyId=1228369860846092671, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 Research Institute of Underground Space, CCCC Tunnel Engineering Co., Ltd., Beijing 100102, China), AuthorCompanyExt(id=1228369860858675586, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, companyId=1228369860846092671, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 中交隧道工程局有限公司地下空间科学研究院, 北京 100102)]), AuthorCompany(id=1228369860967727499, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, xref=2, ext=[AuthorCompanyExt(id=1228369860976116108, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, companyId=1228369860967727499, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, 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Shield machine parameters related to segment floating

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盾构机参数类别 盾构机参数 对管片上浮重要性
盾构掘进参数 切口压力/MPa 主要因素
刀盘扭矩/(kN·m) 主要因素
盾构机总推力/kN 主要因素
刀盘旋转速度/(r·min-1) 次要因素
盾构机推进速度/$(\mathrm{m}\mathrm{m}·\mathrm{m}\mathrm{i}{\mathrm{n}}^{-1})$ 次要因素
盾构姿态参数 俯仰角/(°) 主要因素
偏航角/(°) 次要因素
滚动角/(°) 次要因素
盾尾注浆参数 x号管A液注浆压力/bar 主要因素
x号管B液注浆压力/bar 主要因素
x号管A液注浆量/m3 次要因素
x号管B液注浆量/m3 次要因素
), ArticleFig(id=1228369869054345924, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, language=CN, label=表1, caption=

与管片上浮相关的盾构机参数

, figureFileSmall=null, figureFileBig=null, tableContent=
盾构机参数类别 盾构机参数 对管片上浮重要性
盾构掘进参数 切口压力/MPa 主要因素
刀盘扭矩/(kN·m) 主要因素
盾构机总推力/kN 主要因素
刀盘旋转速度/(r·min-1) 次要因素
盾构机推进速度/$(\mathrm{m}\mathrm{m}·\mathrm{m}\mathrm{i}{\mathrm{n}}^{-1})$ 次要因素
盾构姿态参数 俯仰角/(°) 主要因素
偏航角/(°) 次要因素
滚动角/(°) 次要因素
盾尾注浆参数 x号管A液注浆压力/bar 主要因素
x号管B液注浆压力/bar 主要因素
x号管A液注浆量/m3 次要因素
x号管B液注浆量/m3 次要因素
), ArticleFig(id=1228369869150814916, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, language=EN, label=Table 2, caption=

Summary of statistical analysis of data for the engineering component

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统计参数 最大值 最小值 平均值 标准差
埋深/m 50.10 11.05 32.90 10.91
切口压力/MPa 0.722 0.157 0.487 0.149
刀盘转速/(r·min-1) 1.42 0.65 1.06 0.12
盾构机推进速度/
(mm·min-1)		 50.99 4.51 26.51 6.35
刀盘扭矩/(kN·m) 38 310.00 3 100.67 13 706.81 6 315.05
盾构机总推力/kN 242 691.20 57 718.76 122 826.42 38 013.26
俯仰角/(°) 1.54 -1.79 0.02 1.03
偏航角/(°) 0.24 -0.11 0.07 0.07
滚动角/(°) 0.46 -0.17 -0.01 0.04
), ArticleFig(id=1228369869259866824, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, language=CN, label=表2, caption=

工程部分数据的统计分析总结

, figureFileSmall=null, figureFileBig=null, tableContent=
统计参数 最大值 最小值 平均值 标准差
埋深/m 50.10 11.05 32.90 10.91
切口压力/MPa 0.722 0.157 0.487 0.149
刀盘转速/(r·min-1) 1.42 0.65 1.06 0.12
盾构机推进速度/
(mm·min-1)		 50.99 4.51 26.51 6.35
刀盘扭矩/(kN·m) 38 310.00 3 100.67 13 706.81 6 315.05
盾构机总推力/kN 242 691.20 57 718.76 122 826.42 38 013.26
俯仰角/(°) 1.54 -1.79 0.02 1.03
偏航角/(°) 0.24 -0.11 0.07 0.07
滚动角/(°) 0.46 -0.17 -0.01 0.04
), ArticleFig(id=1228369869394084556, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, language=EN, label=Table 3, caption=

Material parameters of pipe sections and grouting layers

, figureFileSmall=null, figureFileBig=null, tableContent=
材料 选用模型 重力密度γ/
(kN·m-3)		 弹性模量
Es/MPa		 泊松比
μ
管片 弹性 25 28 800 0.2
注浆层(软) 弹性 24 4.8 0.34
注浆层(硬) 弹性 24 10.8 0.32
), ArticleFig(id=1228369869524107984, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, language=CN, label=表3, caption=

管片和注浆层材料参数

, figureFileSmall=null, figureFileBig=null, tableContent=
材料 选用模型 重力密度γ/
(kN·m-3)		 弹性模量
Es/MPa		 泊松比
μ
管片 弹性 25 28 800 0.2
注浆层(软) 弹性 24 4.8 0.34
注浆层(硬) 弹性 24 10.8 0.32
), ArticleFig(id=1228369869603799762, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, language=EN, label=Table 4, caption=

Model error metrics

, figureFileSmall=null, figureFileBig=null, tableContent=
模型 MAE RMSE R2
上浮预测模型 0.980 1.307 0.903
参照模型 1.281 1.685 0.838
), ArticleFig(id=1228369869675102935, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1228279679774294142, language=CN, label=表4, caption=

模型误差指标

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模型 MAE RMSE R2
上浮预测模型 0.980 1.307 0.903
参照模型 1.281 1.685 0.838
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基于数据增强的盾构隧道管片上浮智能预测模型
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汪洋 1 , 王义盛 1 , 蔡琦 1 , 刘朝辉 2 , 王赶 2, *
科学技术与工程 | 论文·交通运输 2025,25(22): 9568-9577
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科学技术与工程 | 论文·交通运输 2025, 25(22): 9568-9577
基于数据增强的盾构隧道管片上浮智能预测模型
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汪洋1 , 王义盛1, 蔡琦1, 刘朝辉2, 王赶2, *
作者信息
  • 1 中交隧道工程局有限公司地下空间科学研究院, 北京 100102
  • 2 北京交通大学土木建筑工程学院, 北京 100044

    • 汪洋(1991—),男,汉族,安徽芜湖人,硕士,高级工程师。研究方向:盾构隧道施工及智能建造技术。E-mail:。

通讯作者:

* 王赶(1997—),男,汉族,安徽安庆人,博士后。研究方向:盾构隧道智能建造。E-mail:。
Intelligent Prediction and Control Optimization Methods for Floating of Shield Tunnel Segments
Yang WANG1 , Yi-sheng WANG1, Qi CAI1, Zhao-hui LIU2, Gan WANG2, *
Affiliations
  • 1 Research Institute of Underground Space, CCCC Tunnel Engineering Co., Ltd., Beijing 100102, China
  • 2 School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
出版时间: 2025-08-08 doi: 10.12404/j.issn.1671-1815.2406374
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摘要
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针对盾构工程中管片上浮预测的需求与深度学习模型训练数据不足的难题,结合盾构机掘进机制与管片上浮过程提出了一套盾构管片上浮预测模型;利用数值模拟软件模拟盾构管片上浮过程,将海量数值模拟数据与工程现场数据同时用于深度学习训练,实现针对管片上浮预测模型的数据增强。其中预测模型由管片上浮预测模型和两个辅助模型组成,考虑了主动控制和被动响应参数的相互作用。最后基于北京东六环入地改造工程盾构段展开典型事例研究。研究结果表明,模型预测精度控制在4 mm以内,满足工程需求;盾尾注浆参数对模型性能的影响最大,其次是掘进参数,而盾构姿态参数的影响最小;基于数值模拟数据的管片上浮训练数据能提升预测模型的预测精度30%,证明了该数据增强方法的有效性,本文提出的基于数值模拟数据的数据增强方法,为类似深度学习模型的训练与优化提供了新的思路。

盾构隧道  /  管片上浮  /  人工智能  /  数据增强

In order to meet the demand for segment floating prediction in shield construction and the problem of insufficient training data for deep learning models, a set of shield segment floating prediction model was proposed by combining the tunneling mechanism of the shield machine with the process of segment floating.The numerical simulation software was used to simulate the process of segment floating of the shield structure, and using the large amount of numerical simulation data and the engineering field data for the deep learning training, so as to realize the data enhancement of the segment floating prediction model. The prediction model consists of the tube sheet floating process. The prediction model consists of a segment floating prediction model and two auxiliary models, which consider the interaction of active control and passive response parameters. Finally, a typical case study was carried out based on the shield section of the Beijing East 6th Ring Road Rehabilitation Project, and the results show that the prediction accuracy of the model is controlled within 4 mm, which meets the project requirements. The grouting parameters of the shield tail have the greatest influence on the model performance, followed by the digging parameters, and the shield attitude parameters have the smallest influence. Moreover, the training data of the segment floating based on the numerical simulation data can improve the prediction accuracy of the prediction model by 30%, which proves the effectiveness of the data enhancement method. The effectiveness of the data enhancement method is demonstrated. The data enhancement method based on numerical simulation data proposed in the article provides a new idea for the training and optimization of similar deep learning models.

shield tunnel  /  segment floating  /  artificial intelligent  /  data augmentation
汪洋, 王义盛, 蔡琦, 刘朝辉, 王赶. 基于数据增强的盾构隧道管片上浮智能预测模型. 科学技术与工程, 2025 , 25 (22) : 9568 -9577 . DOI: 10.12404/j.issn.1671-1815.2406374
Yang WANG, Yi-sheng WANG, Qi CAI, Zhao-hui LIU, Gan WANG. Intelligent Prediction and Control Optimization Methods for Floating of Shield Tunnel Segments[J]. Science Technology and Engineering, 2025 , 25 (22) : 9568 -9577 . DOI: 10.12404/j.issn.1671-1815.2406374
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近年来,盾构法施工由于其在施工速度、施工安全与自动化程度等方面的优势而被广泛应用在城市地下隧道建设中。目前,盾构工程施工中主要有地表沉降与管片上浮两项重要的工程安全指标[1],由于管片上浮现象相较于其他隧道更加严重。过大的管片上浮会造成管片错台、管片开裂、隧道漏水等盾构隧道工程病害,对盾构隧道服役期性能造成严重的不良影响。因此针对盾构管片上浮,建立精确的预测方法与有效的控制方法对保证盾构隧道的施工期与服役期安全至关重要。
目前针对盾构隧道管片上浮的主要研究方法主要包括:理论分析、数值模拟、模型试验和现场试验。在理论分析方面,主要有建立理论模型与分析影响因素两种研究方法。在理论模型的建立中,学者们针对性考虑静态上浮力与动态上浮力[2]、盾构同步注浆填充率[3]、土拱效应[4]和等效连续梁理论[5]分别建立了对应的力学解析模型,对工程中控制管片上浮起到了指导作用。在上浮因素的研究中,学者们将影响因素划分为隧道直径、水文地质条件、覆土厚度、浆液凝固时间、浆液密度、盾构掘进速度、盾构姿态及盾尾间隙等[6-8],并将上浮过程进行了阶段性划分[9-10]。在数值模拟分析方面,现有研究多基于有限元分析针对注浆压力[11]、浆液凝固特性[12-13]和埋深[14]等因素进行研究,揭示管片上浮发展规律。研究发现管段下部注浆压力越大同步注浆凝固时间越长,埋深在15~21 m区间内埋深越大上浮量越大。在模型试验方面,学者们多针对浆液配比[15]与注浆压力[16]开展实验,研究发现浆液的材料配比对管片上浮起着关键作用,其中水胶比和胶砂比和浆液的稠度、流动度和初凝时间正相关,粉水比则呈现负相关。注浆压力理论计算值在0.15~0.18 MPa,与实测数据吻合较好。在现场试验方面,研究人员多关注于盾构施工参数与同步注浆配比等现场易采集的数据进行研究[17],并依据实测数据将上浮过程划分为两个阶段,提出了一系列管片上浮控制措施[18-19]。
当前理论分析方法中推导假设过多,与工程问题有所偏差,数值模拟分析方法也不统一,计算结果差异较大。而近年来,深度学习技术飞速发展,被广泛应用于各类工程问题当中,对于管片上浮研究是一种新的手段。Ye等[20]使用了粒子群优化算法(particle swarm optimization, PSO)和遗传算法(genetic algorithm, GA)等4个机器学习算法预测管片上浮,对比分析了各算法的优缺点,但对于变量之间的互相影响考虑有所欠缺。苏恩杰等[21]提出一种基于卷积神经网络于长短时神经网络的混合模型实时预测盾构掘进管片上浮。但该神经网络模型需要动力水准仪的自动化监测的支持,而实际盾构工程应用中,无法为每一环管片安装自动化监测设备。
综上所述,近年来许多学者针对盾构管片上浮问题开展研究,并取得了系列成果。但目前大部分盾构施工管片上浮预测方法依赖于理论解析方法,工程上亟须建立针对盾构管片上浮预测的深度学习智能模型。但深度学习模型需要海量的工程数据进行训练以获得满足工程要求的预测精度,并且训练数据的多样性严重影响模型对不同工程的适用性与泛化性,故工程中同样亟需解决深度学习模型训练数据量不足的难题。
因此针对盾构工程中管片上浮预测的需求与智能模型训练数据不足的难题,现结合盾构机掘进机制与管片上浮过程提出一套盾构管片上浮预测模型。同时,针对深度学习模型训练数据不足导致的模型准确性与泛化性问题,利用有限元数值分析数值模拟软件模拟盾构管片上浮过程,创新性地将海量数值模拟数据与工程现场数据同时用于管片上浮预测模型训练,实现针对管片上浮预测模型的数据增强。考虑到实际盾构施工过程中主动控制参数与被动响应参数的相互作用机制,该预测模型结构设计包括负责管片上浮预测的主模型与两个分别负责预测推力与扭矩和注浆压力的辅助模型。最终该预测模型成功应用于北京东六环入地改造盾构段工程当中。
1 盾构管片上浮智能预测模型
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1.1 智能模型特征参数选取
盾构掘进的过程中,管片上浮大致可以划分为3个阶段:第一阶段为管片在盾壳内部拼装完成到脱出盾壳尾部,期间主要受盾构机推力油缸的协同作用;第二阶段中,盾尾同步注浆,此时浆液处于液态,管片收到浆液的压力,直到浆液凝固;第三阶段,浆液凝固,土体、浆液与管片形成整体,由于管片的重力小于原有土体而逐渐上浮。
根据管片上浮的3个阶段,与管片上浮相关的影响因素可分为几何因素、地质因素、盾构掘进参数、盾构姿态参数、盾尾注浆参数等。其中,盾构掘进参数、盾构姿态参数和盾尾注浆参数统称为盾构机参数因素。不同的因素在管片上浮的不同阶段起主要作用。几何因素在整个上浮过程中都起作用,而地质因素主要在第三阶段起作用。盾构掘进参数和盾构姿态参数主要在第一阶段起作用且非主要影响参数,而盾尾注浆参数则在第二阶段起作用,盾构掘进参数中,切口压力与盾构机总推力越大,等效为悬臂梁模型的拼接管片越难以产生挠度;刀盘扭矩与刀盘旋转速度会影响土层的扰动程度,土层稳定性受到影响导致管片上浮;盾构机推进速度影响既有管片浆液凝固程度,多环管片浆液均未凝固时也会导致管片上浮。盾构姿态参数中,俯仰角、偏航角、旋转角会影响盾构机姿态,千斤顶合力与轴线产生偏差,管片安装空间发生变化,从而影响管片上浮。除上述因素外,还有一些工程措施也能够影响管片上浮,如二次注浆、应急沙袋和更换注浆材料配比等。但这些措施的时间成本和经济成本都很高,故只有在管片上浮达到严重程度时才会有选择性地使用。这些措施与本文的控制全线管片上浮的目标不同,因此并不在本文方法中考虑。
几何因素主要包括管片外径和隧道埋深,其中,隧道埋深是指地表与管片中心之间的垂直距离。地质因素主要包括管片上方与下方地层的物理特征参数和地下水位。然而,在工程实践中往往难以获得有关地层物理参数的详细信息。以往针对地质参数的研究往往将土壤划分为不同的类别,然后将不同类别土壤所占比例输入智能预测模型中。本文研究基于无监督机器学习将土壤分为5类,聚类分析中的土壤划分为5类,这5类正好对应地质学中的五种典型土壤类型,即填土、黏土、粉土、砂土和岩石。将每个土壤类别的厚度都作为输入参数,本文研究考虑了管片上方与下方的地层厚度占比,再加上地下水位,共同构成11个地质参数。
与几何和地质参数不同,在预测单个隧道管片上浮时,需要同时考虑多环的盾构机参数。相邻两个管片之间的几何和地质参数通常变化极小,而这些管片的盾构机参数可能会有很大差异。故在预测管片的管片上浮时,应同时考虑前两个管片、预测的管片和随后两个管片的盾构参数。在文章提出的方法中,同时采用了5个相邻管片的盾构参数作为输入参数。
基于管片等效悬臂梁模型的受力分析,盾构机参数中部分力学参数在管片上浮过程中起主要作用,其他部分参数作用较小。主次要因素划分如表1所示。其中,切口压力、刀盘扭矩、盾构机总推力、俯仰角和盾尾注浆压力为主要影响因素,刀盘旋转速度、盾构机推进速度,偏航角、滚动角注浆量作为次要影响因素。盾构机俯仰角直接影响盾构机竖向位置,切口压力与盾构机总推力影响管片间的挤压力,挤压力越大越不容易产生上浮。刀盘扭矩会影响盾构机姿态,姿态的调整会对土体产生较大扰动,注浆空腔增大,使管片上浮情况加重。故上述因素为主要影响因素。
1.2 管片上浮智能预测模型与辅助模型
工程中,并不是所有的盾构机参数的取值可以自由组合,部分的盾构机参数存在相互作用。如盾构机总推力和刀盘扭矩,并非由盾构机操作员直接决定。相反,这些参数受到盾构机操作员的主动控制和周围土壤特性的影响,同样的,在盾尾注浆过程中,盾构的注浆量是主动受盾构机操作人员的主动控制,而注浆压力则是同时受注浆量、周围土体性质与盾尾间隙大小影响的。如果输入到深度学习模型中的盾构机参数不符合主动参数与被动参数的相互作用关系,深度学习模型的预测精度必然不高。为此,基于实际盾构施工过程中主动控制参数与被动响应参数的相互作用机制,本文研究建立的盾构管片上浮控制优化模型由3个模型组成,其结构如图1所示。这3个模型包括管片上浮预测模型、扭矩和推力预测模型以及注浆压力预测模型。其中管片上浮预测模型为主要模型,对应管片、盾构机和周围土壤之间的相互作用机制,生成最终上浮预测值。辅助模型为扭矩和推力预测模型以及注浆压力预测模型,起到参数间互相影响的作用,扭矩和推力预测模型对应盾构机刀盘与盾构机前方土壤的相互作用机制,注浆压力预测模型对应管片与周围土壤的相互作用机制。在训练中,初始数据为地质参数、几何参数、用于控制其他目标的主动控制参数、用于控制管片上浮的主动操作参数,将初始数据分类导入扭矩与推力预测模型和盾尾注浆压力预测模型进行训练,分别得到用于其他控制目标的被动响应参数预测值与用于控制管片上浮的被动响应参数预测值。在主要模型的训练中将上述被动响应参数预测值与初始数据共同作为训练数据进行模型训练,最终得出管片上浮预测值。3个模型的具体训练过程如下。
1.2.1 智能预测模型
本文研究建立的管片上浮智能预测模型采用了卷积神经网络(convolutional neural networks, CNN)和深度神经网络(deep neural networks, DNN)。DNN用于从几何和地质参数中提取特征信息,CNN用于从盾构操作参数中提取潜在信息。为提高模型预测准确性,采用了两种技术:Dropout和残差网络。
管片上浮预测模型利用数值模拟数据进行训练以实现数据增强。为此,管片上浮预测模型需要分别考虑哪些盾构掘进参数可以在数值模拟中被考虑,哪些不可以考虑。数值模拟中可模拟的盾构操作参数包括切口压力、刀盘扭矩和总推力;而不可模拟的盾构操作参数则无法在数值模拟中被考虑,包括刀盘转速和推进速度。管片上浮预测模型的框架如图2所示。
鉴于盾构掘进参数的时空特性,一个卷积神经网络(CNN)处理可模拟的盾构操作参数,另一个CNN处理不可模拟的盾构操作参数,还有一个CNN处理盾尾注浆参数。几何和地质参数构成一个大小为1×13的数组。5个管片的盾构掘进姿态参数编码为5×8的数组。此外,5个管片环的盾尾注浆参数被编码为5×4×8的数组。DNN1、CNN1和CNN2的特征经过展平后连接形成新的数组,形成一个大小为1×40的数组。然后,DNN2用于提取拼接得到的数组内的潜在信息,并输出预测的管片上浮量。
1.2.2 辅助模型
1)扭矩和推力预测模型
扭矩和推力预测模型是利用几何参数、地质参数和用于其他控制目标的主动控制参数来预测推力和扭矩。扭矩和推力预测模型的框架如图3所示。
扭矩和推力预测模型主要使用了3个DNN。通用DNN用于从输入参数中提取共同特征,得到一个共同特征。随后,扭矩DNN和推力DNN利用共同特征分别预测扭矩和推力。之后,分别计算扭矩和推力的损失函数。扭矩和推力的损失函数相加得出总损失函数,用于更新这3个DNN中的可训练参数。经过训练后,通用DNN和扭矩DNN构成扭矩预测模型,而通用DNN和推力DNN则构成推力预测模型。所提出的模型既考虑了推力预测与扭矩预测的相似性,同时也考虑了两者间的差异性。通用DNN侧重于提取扭矩和推力预测之间共同的潜在信息,而扭矩和推力DNN则分别针对性的提取与扭矩和推力预测相关的特定潜在信息。
2)注浆压力预测模型
注浆压力预测模型用于根据注浆量、地质参数和隧道直径来预测注浆压力。双向门循环神经网络(Bi-GRU)用于从输入参数中提取潜在特征。Bi-GRU能够考虑不同注浆管道的空间特征,并输出每个注浆管道的特征。然后使用DNNA和DNNB处理每个注浆管的特征并输出A液与B液注浆压力。这些DNN建立了各管道特征与注浆压力之间的关系。注浆压力预测模型框架如图4所示。Bi-GRU用于提取输入参数中的共同潜在信息,而DNNA和DNNB则分别考虑A液注浆压力和B液注浆压力预测的特殊性。
2 模型训练数据增强
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2.1 管片上浮数值模拟
为获取大量用于深度学习模型训练的数值模拟数据,本文研究采用商业有限元Abaqus进行盾构掘进过程中管片上浮的数值模拟。但是由于数值模拟方法的固有缺点,无法模拟所有的盾构机参数,故本文的数值模拟中考虑了1.1节中的所有主要影响因素,而忽略次要影响因素。
数值模拟模型分为3个部分:管片、注浆层以及土体。各部分之间采用绑定约束,管片各环之间采用硬接触。为了模拟浆液的凝固,采用温度场的方法,对浆液的弹性模量与泊松比进行渐进的控制。浆液压力的模拟则是对注浆层施加了一个向外扩张的场力,实际工程中一环管片上有许多注浆孔,可将注浆层按注浆孔个数进行区域划分,不同注浆孔附近可施加不同压力,以达到模拟实际注浆工况的效果。
数值模拟模型的工作步设置上,按照一般盾构工法的施工顺序进行设置。首先对掌子面进行约束,模拟盾构机掌子面的压力施加。接着开始对土进行掘进,使用生死单元法,杀死土体后,对隧道内壁进行约束,模拟盾构机的支撑作用。当管片从盾尾脱出后,使用生死单元法,激活管片单元与注浆层,撤去内壁约束。如此反复掘进,在掘进过程中,每掘进一环,将浆液的物理性质改变一次,逐渐达到凝固后的物理性质。模型计算完毕后,生成管片上浮云图,作为管片上浮预测模型的数据增强的训练样本。
2.2 基于数值模拟数据的模型训练
本文研究采用数值模拟数据对深度学习模型训练数据进行数据增强,但由于数值模拟方法的固有缺点,仅能考虑几何参数、地质参数和对管片上浮起主要作用的盾构机参数,部分次要影响的盾构机参数无法考虑,故数值模拟数据无法直接用于深度学习模型的训练。针对这个问题,制定特定的深度学习流程,如图5所示。
首先,进行海量的数值模拟,收集模型的数据与计算结果,形成用于数据增强的数值模拟数据库。然后,用数值模拟中的全部数据进行深度学习训练。由于数值模拟数据库中的输入参数只包括几何参数,地质参数与主要盾构机参数,所以训练的深度学习模型中网络结构只包括编码器1、编码器2、解码器4与假解码器,深度学习模型也只考虑几何参数、地质参数、可模拟盾构参数与盾尾注浆参数。然后,将工程中获取的相对较少的训练数据划分为3份,分别为训练集,验证集和测试集,比例为3∶1∶1。
用训练集的工程数据开展模型训练,验证集的数据用于测试模型是否过拟合。在训练开始之前,编码器1、编码器2与编码器4预先加载了使用数值模拟数据训练的模型,模型已经学会如何提取几何参数、地质参数、可模拟盾构参数与盾尾注浆参数中的潜在特征信息;而编码器3和解码器则是随机初始化的。在模型训练期间,编码器1、编码器2和编码器4的学习率设定为其他网络结构的1/10。最后,在测试集上测试完全训练好的段浮动预测模型,以评估其性能。
3 典型事例研究
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3.1 工程数据
本文研究收集了北京东六环入地改造工程盾构段施工过程中的几何参数、地质参数、盾构运行参数和管片上浮情况,用于评估文章提出的管片上浮预测模型和优化控制模型。该隧道工程地质剖面图如图6所示,其全长约7.3 km,工程被中间竖井划分为两个主要部分:隧道北段和隧道南段。隧道北段和南段分别长约2.6 km和4.8 km。隧道外径为16.07 m,较多穿越富水砂层。大直径和富水砂层的组合加剧了工程中段面上浮的程度。隧道埋深介于11.05~50.1 m。部分几何参数和盾构操作参数的统计分析如表2所示。此外,图7显示了管片上浮量的分布。
3.2 数值模拟数据
采用东六环项目施工参数进行数值模拟建模,其中管片采用薄壁圆管,模型管片共3环,每环管片宽2 m,外径7.7 m,内径7.05 m,壁厚0.65 m。隧道轴线平均埋深12 m,取开挖土体单元长度为2 m。土体采用矩形模块,模型长12 m,宽32 m,高32 m。注浆层采用薄壁圆管,其外径为8 m,内径为7.7 m,厚度0.3 m。土体结构模型采用Mohr-Coulomb模型,土体、管片、注浆层均采用C3D8R实体单元(三维实体8节点单元),模型共7 920个单元。依据盾构隧道开挖特性,将开挖方向盾构机与土体接触面作为XOY平面,水平方向为x轴方向,垂直方向为y方向,轴线方向为z轴方向(盾构掘进方向为z轴正方向)。上浮模拟模型中主要材料有土、管片、注浆层3种。其中,土体材料参数根据不同类型土质的性质特征在合理范围内随机取值范围取值;管片与注浆层的材料参数为定值,如表3所示。
基于以上所选取的盾构施工中各部件参数和3种类型的单元,建立管片上浮有限元模型。土体、管片和注浆层模型如图8所示。
基于所建立的有限元网格图,对开挖过程进行模拟。首先为避免模拟计算过程中出现刚体位移,在施加约束时,将第1环管片与注浆层的x方向施加位移为0的约束,并在管片与管片、注浆层与注浆层之间设置硬接触,管片与注浆层、注浆层与土体之间设置绑定约束。第2步,为实现注浆压力的模拟,将注浆层分为8个区域,分别施加向外扩张的场力。第3步,利用温度场的切换实现浆液的凝固过程。第4步,进行开挖计算。过程中,利用生死单元法模拟开挖。在盾构掘进中,每开挖1环,对环周土体施加全约束,模拟盾构机对土体的支撑作用,并对掌子面施加z方向位移为0的约束,模拟盾构机的顶推力。当开挖至第3环时,盾尾脱出,生成第1环管片与注浆层,取消环周全约束。开挖至第4环时,生成第2环管片与注浆层,第1环注浆层凝固,以此类推至开挖3环结束,所有注浆层凝固。计算完毕后得到土层、管片的位移和应力等重要数据。通过对管片上浮数值模型进行计算,得到管片竖向位移云图,如图9所示。
3.3 管片上浮预测精度分析
在预测一环管片的管片上浮时,上浮预测模型需要同时输入相邻5环管片的盾构运行参数到。为了研究考虑5环管片对模型预测精度的影响,本文研究构建了一个参照模型。参照模型仅考虑当前预测一环管片的盾构运行参数,然后将参照模型与本文研究中的管片上浮预测模型进行比较。图10对比本文提出的管片上浮预测模型和参照模型的预测结果,可以看出本文提出的管片上浮预测模型具有更好的预测效果。此外表4中使用平均绝对误差(mean absolute error,MAE)、均方根误差(root mean square error,RMSE)和相关系数(R2)三个模型性能指标参数对两个模型的预测效果进行评估。具体而言,管片上浮预测模型的R2=0.903,参照模型的R2=0.838。同样,管片上浮预测模型的MAE和RMSE也均小于参照模型。根据MSE值,管片上浮预测模型的预测精度相对于参照模型提高30%。对比结果证明了考虑五环盾构运行参数可以有效提升管片上浮预测模型预测精度。
3.4 盾构操作参数的敏感性分析
为了研究盾构运行参数的影响,进行了敏感性分析。敏感性分析结果如图11所示。蓝色、绿色和原色分别代表不同类别的盾构机参数、盾构掘进参数、盾构姿态参数和尾部注浆参数。从分析中可以看出,尾部注浆参数总体上表现出最高的灵敏度,B液注浆量在所有参数中表现出最大的影响。盾构掘进参数也具有显著的敏感性,尤其是切削压力和总推力。相比之下,盾构姿态参数的灵敏度最低,俯仰角、偏航角和滚转角的灵敏度均为中等(0.28~0.29)。这表明尾部注浆参数的作用最为关键,而盾构姿态参数的影响相对较小。
3.5 数据增强效果分析
为检验数值模拟环节提供的训练数据是否有效,在模型训练时调用不同数值模拟数据量对模型进行训练,并分析对应的上浮预测精度,分析结果如图12所示。研究发现,当预训练时调用的数值模拟数据量越大时,管片上浮预测模型的MAE和RMSE越小,R2越大,例如,当没有采用数值模拟进行数据增强时,管片上浮预测模型的MAE、RMSE以及R2分别为4.295、4.911、0.69,将所有数值模拟数据进行数据增强训练后3个指标分别为0.98、1.307、0.903,管片上浮预测模型的MAE、RMSE值显著降低,R2显著上升,表明数值模拟所提供的训练数据对该上浮预测模型的精度起着较大的正面作用。
4 结论
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针对盾构工程中管片上浮控制的需求,提出了一套管片上浮预测模型与管片上浮控制优化方法。该管片上浮预测模型考虑了主动控制和被动响应参数之间错综复杂的相互作用,数据流转符合工程实际,建立了管片上浮预测模型和两个辅助模型。同时,针对深度学习模型训练数据不足导致的模型准确性与泛化性问题,本文研究中利用有限元数值分析数值模拟软件模拟盾构管片上浮过程,创新地性将海量数值模拟数据与工程现场数据同时用于管片上浮预测模型训练,实现针对管片上浮预测模型的数据增强。并且基于该套管片上浮预测模型,结合工程现场人员盾构机控制的显示情况,建立管片上浮控制优化方法。此外于北京东六环入地改造工程盾构段展开研究,得出以下结论。
(1)所提出的管片上浮预测模型能够很好地预测管片上浮。与仅考虑预测环的盾构运行参数的预测模型相比,本文的预测模型由于考虑多环管片的盾构机操作参数而具有更高的预测精度,对比之下,预测精度上有30%的显著提高。
(2)采用数值模拟数据进行模型数据增强后,管片上浮预测模型的MAE与RMSE均降低,R2增大,模型预测效果得到显著提升。
(3)全局敏感性分析表明,盾尾注浆参数,特别是B液注浆量,对模型性能的影响最大,其次是掘进参数,如切口压力和总推力。而盾构姿态参数的影响最小,这突出了优化注浆和掘进参数以提高性能的必要性。
基金
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  • 国家自然科学基金((U1934210)
  • 中交一公局集团有限公司品牌工程科技研发(PPZX-2022-14)
文献
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2025年第25卷第22期
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doi: 10.12404/j.issn.1671-1815.2406374
  • 接收时间：2024-08-25
  • 首发时间：2026-02-11
  • 出版时间：2025-08-08
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  • 收稿日期：2024-08-25
  • 修回日期：2025-04-29
基金
国家自然科学基金((U1934210)
中交一公局集团有限公司品牌工程科技研发(PPZX-2022-14)
作者信息
    1 中交隧道工程局有限公司地下空间科学研究院, 北京 100102
    2 北京交通大学土木建筑工程学院, 北京 100044

通讯作者:

* 王赶(1997—),男,汉族,安徽安庆人,博士后。研究方向:盾构隧道智能建造。E-mail:。
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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