Article(id=1222940992664227870, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1222940980886626474, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20230211, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1676563200000, receivedDateStr=2023-02-17, revisedDate=1679241600000, revisedDateStr=2023-03-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1769501453796, onlineDateStr=2026-01-27, pubDate=1700841600000, pubDateStr=2023-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769501453796, onlineIssueDateStr=2026-01-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769501453796, creator=13041195026, updateTime=1769501453796, updator=13041195026, issue=Issue{id=1222940980886626474, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='11', pageStart='1', pageEnd='226', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769501450987, creator=13041195026, updateTime=1769501450987, updator=13041195026, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=73, endPage=76, ext={EN=ArticleExt(id=1222940992978800703, articleId=1222940992664227870, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Intelligent Optimization Support Vector Machine Model for Concrete Extra-high Arch Dam Deformation Monitoring, columnId=1222925280734335368, journalTitle=Water Resources and Power, columnName=DAM SAFETY AND MONITORING, runingTitle=null, highlight=null, articleAbstract=

The arch dam deformation monitoring model is the most commonly used method for arch dam health monitoring. Aiming at the deformation monitoring problem of extra-high arch dams, this paper proposes an intelligent optimization support vector machine deformation monitoring model for concrete extra-high arch dams. Particle swarm optimization (PSO) was used to optimize the penalty factor, kernel function parameters of the support vector machine (SVM), and tolerate bias. The deformation monitoring model of concrete extra-high arch dam based on PSO-SVM was established, and the influence of aging factors on the model performance was analyzed. Engineering examples show that the PSO-SVM deformation monitoring model of concrete extra-high arch dam has good prediction accuracy and generalization ability, which is suitable for deformation monitoring of extra-high arch dam.

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拱坝变形监测模型是拱坝健康监测中最常用的方法。针对特高拱坝的变形监测问题，提出了一种混凝土特高拱坝的智能优化支持向量机变形监测模型。采用粒子群算法（PSO）优化支持向量机（SVM）的惩罚因子、核函数参数及容忍偏差，建立基于PSO-SVM的混凝土特高拱坝变形监测模型，分析了时效因子对模型性能的影响。工程实例表明，混凝土特高拱坝的PSO-SVM变形监测模型有着良好的预测精度及泛化能力，适用于特高拱坝的变形监测。

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王鹏（1996-），男，硕士研究生，研究方向为大坝安全监测与数值分析，E-mail：wangpeng134987@163.com

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王鹏（1996-），男，硕士研究生，研究方向为大坝安全监测与数值分析，E-mail：wangpeng134987@163.com

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