Article(id=1281616675074183409, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.02.0105, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1758384000000, receivedDateStr=2025-09-21, revisedDate=1764777600000, revisedDateStr=2025-12-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1783490826623, onlineDateStr=2026-07-08, pubDate=1772208000000, pubDateStr=2026-02-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783490826623, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783490826623, creator=13701087609, updateTime=1783490826623, updator=13701087609, issue=Issue{id=1281616655499366465, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='2', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1772208000000', pubDateStr='2026-02-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783490821957, creator='13701087609', updateTime=1783588693138, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027157069218127, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027157069218128, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=66, endPage=76, ext={EN=ArticleExt(id=1281616675309064434, articleId=1281616675074183409, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Online monitoring of shaft structure performance driven by digital twins, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

To address the current issues of low intelligence level in coal mine working faces and insufficient research on the performance monitoring of shaft structures, a digital twin-based performance monitoring method for vertical shafts is proposed. Firstly, a five-dimensional framework for the digital twin of vertical shafts is proposed based on the operational mechanism and performance monitoring requirements of the shafts. Secondly, a digital twin of the shaft is established by combining virtual-real mapping technology with a finite element surrogate model for grid dimensionality reduction. The structural performance of the vertical shaft is predicted online through artificial neural network technology, where the predicted data is the real-time prediction of shaft structure performance data obtained during the shaft operation process using a shaft structure performance prediction model. The prediction model for the structural performance of the vertical shaft adopts the RBF surrogate model, and the Unity3D virtual engine platform is built to integrate the above functions and achieve online prediction of the structure performance of the vertical shaft. The results indicate that during the operation, by simulating 120 sets of stress and strain data under different working conditions, the average coefficient of determination between predicted and simulated values is 0.995 5, indicating a high correlation between the predicted strain and simulated strain, thus verifying the feasibility of the digital twin framework for vertical shafts. This provides an effective reference for the digital improvement of vertical shafts.

, authors=Xiaofen JIA¹, Yuchen ZHAO², Baiting ZHAO², Rui HU³, Zhenhuan LIANG³, authorsList=Xiaofen JIA, Yuchen ZHAO, Baiting ZHAO, Rui HU, Zhenhuan LIANG, authorCompany=null, correspAuthors=null, 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=1281616675795603701, articleId=1281616675074183409, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=数字孪生驱动的立井井筒结构性能在线监测, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

为解决当前煤矿工作面智能化程度低、井筒结构性能监测研究较少的问题，提出基于数字孪生的立井井筒结构性能监测方法。首先，根据井筒的运行机制和性能监测的需求，提出立井井筒的数字孪生五维框架；其次，通过虚实映射技术结合网格降维的有限元代理模型建立井筒的数字孪生体；然后，借助人工神经网络技术在线预测立井井筒应力，其中，预测数据是立井井筒运行过程中通过井筒有限元预测模型实时预测的井筒结构性能数据；最后，立井井筒有限元预测模型采用径向基函数（RBF）代理模型，并采用Unity3D虚拟引擎搭建平台，集成上述功能，实现立井井筒结构性能在线预测。结果表明：在井筒运行过程中，通过模拟120组不同工况的应力和应变，预测值和模拟值的平均决定系数为0.995 5，预测应变与模拟应变有较高的相关性，验证了立井井筒数字孪生框架的可行性。

, authors=贾晓芬教授¹, 赵玉晨², 赵佰亭教授², 胡锐³, 梁镇洹³, authorsList=贾晓芬教授, 赵玉晨, 赵佰亭教授, 胡锐, 梁镇洹, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=Xd9avIJeItfRXLLvisTMPQ==, magXml=F2vX6Z2zDbettG60g70yUw==, pdfUrl=null, pdf=SGU9a+ez64W+sUvMME80iQ==, pdfFileSize=2129037, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=PUwy4LXu+2i5oVNNa86V/A==, mapNumber=null, fund=null)}, authors=[Author(id=1281616677821452544, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616675074183409, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=jxfzbt2008@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281616677901144322, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616675074183409, authorId=1281616677821452544, language=EN, stringName=Xiaofen JIA, firstName=Xiaofen, middleName=null, lastName=JIA, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan Anhui 232001, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281616677972447491, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616675074183409, authorId=1281616677821452544, language=CN, stringName=贾晓芬教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹安徽理工大学 煤炭无人化开采数智技术全国重点实验室，安徽 淮南 232001, bio={"content":"

贾晓芬（1978—），女，安徽砀山人，博士，教授，博士生导师，主要从事数字孪生、智能制造、深度学习等方面的研究。E-mail: jxfzbt2008@163.com。

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贾晓芬（1978—），女，安徽砀山人，博士，教授，博士生导师，主要从事数字孪生、智能制造、深度学习等方面的研究。E-mail: jxfzbt2008@163.com。

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