Article(id=1223185961639596363, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223185958363841064, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20220749, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1649952000000, receivedDateStr=2022-04-15, revisedDate=1654444800000, revisedDateStr=2022-06-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1769559858950, onlineDateStr=2026-01-28, pubDate=1679673600000, pubDateStr=2023-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769559858950, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769559858950, creator=13701087609, updateTime=1769559858950, updator=13701087609, issue=Issue{id=1223185958363841064, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='3', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769559858149, creator=13701087609, updateTime=1769561242661, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223191765415477785, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223185958363841064, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223191765415477786, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223185958363841064, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=87, endPage=90, ext={EN=ArticleExt(id=1223185963766108527, articleId=1223185961639596363, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Research on Gross Error Detecting Method of Monitored Dam Deformation Data Based on Fully Convolutional Networks, columnId=1222925280734335368, journalTitle=Water Resources and Power, columnName=DAM SAFETY AND MONITORING, runingTitle=null, highlight=null, articleAbstract=

Goss error detecting method of monitored dam deformation data is researched based on the fully convolutional neural networks (FCN). Firstly, the method of representation learning of artificially labeled data sets by FCN model was proposed to simulate engineers’ experience. Secondly, the FCN model for gross error detection was built and artificially labeled data sets were used for model training. Finally, the trained FCN model was used for gross error detecting of monitored deformation data of a gravity dam. The results show that the gross error in monitored dam deformation data can be accurately obtained by the proposed method, which can improve efficiency of dam safety management.

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针对大坝变形监测数据普遍存在粗差的问题，采用全卷积神经网络（FCN）模型对人工标记数据集进行表征学习的方法实现变形粗差数据识别的人工智能模拟；在此基础上，利用Python和Tensorflow框架构建了用于变形监测数据粗差识别的FCN模型并以人工标注数据集进行模型训练；最后，以训练得到的最优模型对某重力坝变形监测数据进行粗差识别应用。结果表明，经训练的FCN模型能够较准确地识别大坝变形监测数据中的粗差值，提高了大坝安全管理效率。

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齐智勇（1985-），男，硕士、高级工程师，研究方向为大坝安全监测、诊断分析，E-mail：qi_zhiyong@ctg.com.cn

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齐智勇（1985-），男，硕士、高级工程师，研究方向为大坝安全监测、诊断分析，E-mail：qi_zhiyong@ctg.com.cn

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