Article(id=1273953258822471794, tenantId=1146029695717560320, journalId=1272209045839646724, issueId=1273953231114887613, articleNumber=null, orderNo=null, doi=10.20174/j.JUSE.2026.02.07, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1737129600000, receivedDateStr=2025-01-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1781663725810, onlineDateStr=2026-06-17, pubDate=1776614400000, pubDateStr=2026-04-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1781663725810, onlineIssueDateStr=2026-06-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1781663725809, creator=13701087609, updateTime=1781663725809, updator=13701087609, issue=Issue{id=1273953231114887613, tenantId=1146029695717560320, journalId=1272209045839646724, year='2026', volume='22', issue='2', pageStart='377', pageEnd='752', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1781663719203, creator=13701087609, updateTime=1781663760928, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1273953406235467954, tenantId=1146029695717560320, journalId=1272209045839646724, issueId=1273953231114887613, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1273953406235467955, tenantId=1146029695717560320, journalId=1272209045839646724, issueId=1273953231114887613, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=448, endPage=458, ext={EN=ArticleExt(id=1273953259099295860, articleId=1273953258822471794, tenantId=1146029695717560320, journalId=1272209045839646724, language=EN, title=Study on the Prediction of Underground Cavern Rock Deformation Based on GRU Neural Network, columnId=null, journalTitle=Chinese Journal of Underground Space and Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to enhance the prediction accuracy of surrounding rock deformation, enable real-time monitoring of deformation status, prevent deformation failure, and ensure construction safety, a novel underground cavern surrounding rock deformation temporal prediction method based on GRU neural network is proposed to tackle the low training efficiency, slow convergence, and poor generalization of traditional methods, along with the establishment of a corresponding prediction framework. Utilizing monitoring data of surrounding rock deformation from the underground powerhouse on the right bank of the Baihetan Dam, predictions are made and subsequently compared and analyzed with the forecasting results generated by the Long Short-Term Memory (LSTM) neural network algorithm. The results indicate that the GRU neural network model effectively addresses the prediction challenges associated with underground cavern surrounding rock deformation, offering advantages such as simplified structure, relatively fewer parameters, rapid training and convergence rates, and high prediction accuracy. Compared to the predictions derived from the LSTM neural network algorithm, the GRU model demonstrates a reduction in training duration by over 70%, with a corresponding decrease in prediction error of more than 50%. The relative error for cumulative maximum deformation is less than 0.3%, the probability of absolute error less than 0.9 mm is as high as 95%, and the maximum absolute error is only 2.05 mm.

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为了提高围岩变形预测精度，实时掌握变形状态，预防围岩变形破坏，保障施工安全，针对传统围岩变形预测方法训练效率低、收敛速度慢、泛化能力弱等问题，提出了一种基于GRU神经网络的地下洞室围岩变形时序预测方法，构建了相应的围岩变形预测框架流程。结合白鹤滩右岸地下厂房围岩变形监测数据进行预测，并将其与长短期记忆（LSTM）神经网络算法预测结果进行对比分析。结果表明：GRU神经网络模型能够较好地解决地下洞室围岩变形预测问题，具有结构简单、参数量相对较少、训练及收敛速度快、预测精度高等优势。与LSTM神经网络算法预测结果相比，模型训练时长降幅超过70%，预测误差降低幅度高达50%以上，累计最大变形的相对误差小于0.3%，绝对误差小于0.9 mm的概率高达95%，最大绝对误差仅为2.05 mm。

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^2.College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei 443002, P. R. China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1273953260881875087, tenantId=1146029695717560320, journalId=1272209045839646724, articleId=1273953258822471794, authorId=1273953260466638987, language=CN, stringName=万晨, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1．湖北省水电工程智能视觉监测重点实验室，湖北 宜昌 443002
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万晨（2000—），男，武汉人，硕士生，主要从事神经网络技术及应用方面的研究。E-mail: ettalwanc@gmail.com

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万晨（2000—），男，武汉人，硕士生，主要从事神经网络技术及应用方面的研究。E-mail: ettalwanc@gmail.com

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