Article(id=1308453959790776777, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453947069452650, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.01.010, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743091200000, receivedDateStr=2025-03-28, revisedDate=1753286400000, revisedDateStr=2025-07-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889333913, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889333913, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889333913, creator=13701087609, updateTime=1789889333913, updator=13701087609, issue=Issue{id=1308453947069452650, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='1', pageStart='1', pageEnd='165', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789889330879, creator='13701087609', updateTime=1789889788596, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308455866915639450, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453947069452650, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308455866915639451, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453947069452650, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=76, endPage=86, ext={EN=ArticleExt(id=1308453960000491978, articleId=1308453959790776777, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Review of Prediction and Control of Surface Settlement Induced by Shield Tunnelling Based on Machine Learning, columnId=1152669335454658940, journalTitle=Urban Rapid Rail Transit, columnName=Academic Discussion, runingTitle=null, highlight=null, articleAbstract=

Predicting and controlling the surface settlement induced by shield tunneling is a critical engineering issue in underground space development. With the development of artificial intelligence technology, machine learning algorithms have demonstrated significant effectiveness in surface settlement prediction and control. We summarize the theory of machine learning, focusing on four key aspects: database construction, data preprocessing, model selection and evaluation, and shield tunneling parameter optimization under settlement control. Additionally, it critically examines the limitations of existing research in this domain. Current research often neglects the additional impacts of surface structure loads, traffic flow, and pedestrian activities, lacks model interpretability, and struggles to balance prediction accuracy with practical applicability. Future research should focus on developing a comprehensive measurement and evaluation system that incorporates the dynamic effects of surface buildings, traffic, and ground surcharge into settlement prediction models. Furthermore, future work should enhance model interpretability and promote the dynamic updating of machine learning models with real-time monitoring data, thereby providing more scientifically sound guidance for safety control in shield tunneling construction.

, authors=Chuitian LI¹, Xiaoqiong LIN², Ji ZHANG², Kaicheng XU², Mengcheng CHEN², authorsList=Chuitian LI, Xiaoqiong LIN, Ji ZHANG, Kaicheng XU, Mengcheng CHEN, authorCompany=null, correspAuthors=Ji ZHANG, 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, fund=null), CN=ArticleExt(id=1308453960361202123, articleId=1308453959790776777, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于机器学习的盾构隧道掘进地表沉降预测与控制研究, columnId=1152669335668568445, journalTitle=都市快轨交通, columnName=学术探讨, runingTitle=null, highlight=null, articleAbstract=

预测和控制盾构隧道掘进诱发的地表沉降，是地下空间开发的重要工程问题。随着人工智能技术的发展，机器学习算法在地表沉降预测与控制方面取得良好的应用效果。概述机器学习理论，围绕数据库搭建、数据预处理、模型选择与评估、基于沉降控制下的盾构施工参数优化这4个方面展开讨论，分析现有研究的不足之处。当前研究面临忽略地表构筑物荷载、交通流动及行人活动所带来的附加影响等问题，模型缺乏可解释性以及预测准确性与实用性之间的平衡。后续研究应聚焦于构建综合性测量与评估体系将地表建筑、交通及地面堆载的动态效应纳入沉降预测模型，同时加强模型的可解释性，推动实时监测数据与机器学习模型的动态更新，为盾构施工的安全控制提供更加科学的指导。

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李垂忝，男，本科，高级工程师，从事城市轨道交通施工技术与管理，llliinn123@163.com

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李垂忝，男，本科，高级工程师，从事城市轨道交通施工技术与管理，llliinn123@163.com

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