Article(id=1322559973305991268, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747152000000, receivedDateStr=2025-05-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252469611, onlineDateStr=2026-10-29, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252469611, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252469611, creator=13701087609, updateTime=1793252469611, updator=13701087609, issue=Issue{id=1322559951067792299, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='4', pageStart='1', pageEnd='294', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252464310, creator='13701087609', updateTime=1793252763065, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322561204200001631, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322561204200001632, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=84, endPage=93, ext={EN=ArticleExt(id=1322559973511512165, articleId=1322559973305991268, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on Multi-Step Prediction of Roadway Deformation Based on AE-TCN-Transformer Parallel Model, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To achieve accurate prediction of roadway deformation, a parallel prediction model that combines a Temporal Convolutional Network (TCN) and a Transformer, optimized using the Alpha evolution (AE) algorithm was proposed. This model integrated TCN and Transformer architectures through a parallel framework. The TCN utilized dilated causal convolutions to extract local temporal features, while the Transformer employed a multi-head self-attention mechanism to enhance the capture of long-term temporal patterns. Finally, the model output results through a dynamic multi-model feature fusion mechanism guided by temporal attention. At the same time, the AE algorithm was introduced to optimize hyper parameters, leveraging adaptive evolutionary paths and a two-step mechanism to enhance the model's robustness as handling complex time-series data. Experimental validation was conducted using roadway displacement monitoring data from three different monitoring points in the middle levels of an iron ore in Yunnan. The results show that when the prediction step size ranges from 6 to 48, the AE-TCN-Transformer model maintains a root mean square error (RMSE) below 0.013 2 mm, a symmetric mean absolute percentage error (SMAPE) below 0.09%, and a coefficient of determination (R²) above 0.97. These performance metrics significantly outperform those of single models such as TCN, Transformer, LSTM, and other single-model approaches. This fully validates the accuracy and reliability of the AE-TCN-Transformer model in roadway deformation prediction and provides a new approach for precise prediction of roadway deformation.

, authors=Longfei WANG¹, Junzhi CHEN¹, Chunfang REN¹, Chuanchuan CAI¹, Xiaowen XU¹, Yang LIU², Changyao HE², authorsList=Longfei WANG, Junzhi CHEN, Chunfang REN, Chuanchuan CAI, Xiaowen XU, Yang LIU, Changyao HE, authorCompany=null, correspAuthors=Junzhi CHEN, 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=1322559973784141926, articleId=1322559973305991268, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于AE-TCN-Transformer并行模型的巷道变形多步预测研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

为实现巷道变形的精准预测，提出一种基于阿尔法进化算法（AE）优化的时间卷积网络（TCN）和Transformer并行预测模型。该模型通过并行架构融合TCN与Transformer模型，TCN利用膨胀因果卷积挖掘局部时序特征，Transformer通过多头自注意力机制提升长时序模式的捕捉效率，最后通过时序注意力引导的多模型动态特征融合机制输出结果。同时，引入AE算法优化超参数，凭借自适应进化路径与双步长机制，增强模型在复杂时序数据中的鲁棒性。以云南某铁矿三个不同中段监测点的巷道位移监测数据为研究对象进行试验验证，结果表明：AE-TCN-Transformer模型在预测步长为6~48步时，其均方根误差控制在0.013 2 mm以下，对称平均绝对百分比误差低于0.09%，决定系数高于0.97，显著优于TCN、Transformer、LSTM等单一模型，充分验证了AE-TCN-Transformer模型在巷道变形预测中的精确性与可靠性，为巷道变形精准预测提供了新路径。

, authors=王龙飞¹, 陈俊智¹, 任春芳¹, 蔡川川¹, 徐晓文¹, 刘洋², 何昌耀², authorsList=王龙飞, 陈俊智, 任春芳, 蔡川川, 徐晓文, 刘洋, 何昌耀, authorCompany=null, correspAuthors=陈俊智, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=ACk59wq2b/I2Bg8LiTOhbA==, magXml=x8jT/14FT8HmsBkWfGgNVA==, pdfUrl=null, pdf=D0NMcsJWgSo7FTh7j36oRQ==, pdfFileSize=2029934, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=tfnHz02ukoHuCLoP1bnHuQ==, mapNumber=null, fund=null)}, authors=[Author(id=1322559974203572334, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559973305991268, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=wanglongfei0322@foxmail.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322559974283264112, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559973305991268, authorId=1322559974203572334, language=EN, stringName=Longfei WANG, firstName=Longfei, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650093, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322559974346178673, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559973305991268, authorId=1322559974203572334, language=CN, stringName=王龙飞, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．昆明理工大学 国土资源工程学院，云南 昆明　650093, bio={"content":"

王龙飞（2000—），男，陕西宝鸡人，硕士研究生，主要从事矿山岩体力学方面的研究。E-mail:wanglongfei0322@foxmail.com

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王龙飞（2000—），男，陕西宝鸡人，硕士研究生，主要从事矿山岩体力学方面的研究。E-mail:wanglongfei0322@foxmail.com

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