Article(id=1223190867771511082, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222203, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1666195200000, receivedDateStr=2022-10-20, revisedDate=1668441600000, revisedDateStr=2022-11-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1769561028664, onlineDateStr=2026-01-28, pubDate=1695571200000, pubDateStr=2023-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769561028664, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769561028664, creator=13701087609, updateTime=1769561028664, updator=13701087609, issue=Issue{id=1223190866320278179, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='9', 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=1769561028318, creator=13701087609, updateTime=1769562015483, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223195006870082478, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223195006870082479, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=89, endPage=93, ext={EN=ArticleExt(id=1223190868002197807, articleId=1223190867771511082, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Deformation Prediction Model of Concrete Dam Based on EEMD-AEFA-LSTM, columnId=1222925280734335368, journalTitle=Water Resources and Power, columnName=DAM SAFETY AND MONITORING, runingTitle=null, highlight=null, articleAbstract=

Reasonable data mining and accurate prediction and analysis of concrete dam deformation monitoring data are the key means to ensure the safe and long-term operation of the dam. Due to the impact of environmental variables such as temperature and water level, the dam deformation time series has periodic, nonlinear and other change characteristics. Existing intelligent algorithms can not capture the nonlinear relationship of sequences well, A concrete dam deformation prediction model based on EEMD-AEFA-LSTM model was proposed. Ensemble empirical mode decomposition was used to effectively decompose the deformation time series. The long short-term memory network model optimized by the artificial electric field algorithm was used to predict the decomposition components and reconstruct the prediction results. The deformation monitoring data of EX16 and EX24 measuring points of a concrete dam were selected for prediction research. The results show that the prediction accuracy of the EEMD-AEFA-LSTM model is significantly higher than that of the AEFA-LSTM model, PSO-LSTM model, and GA-LSTM model. The average absolute error, mean square error, and root mean square error of the prediction results are the minimum values, providing a new way for accurate prediction of concrete dam deformation.

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对混凝土坝变形监测资料进行合理的数据挖掘和准确的预测分析是确保大坝安全长效运行的关键手段,大坝变形时间序列受到温度、水位等环境量的影响,具有周期性、非线性等变化特征,现有的智能算法无法很好地捕捉序列的非线性关系。对此,提出了基于EEMD-AEFA-LSTM模型的混凝土坝变形预测模型,采用集合经验模态分解EEMD有效分解变形时间序列,通过人工电场算法AEFA优化的长短期记忆网络LSTM模型对各分解分量进行预测并重构预测结果。选取某混凝土坝EX16、EX24测点的变形监测资料开展预测研究。结果表明,所建EEMD-AEFA-LSTM模型的预测精度明显高于AEFA-LSTM、PSO-LSTM、GA-LSTM模型,预测结果的平均绝对误差、均方误差、均方根误差均为最小值,为混凝土坝变形的精确预测提供了新途径。

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郑东健(1965-),男,博士、教授、博导,研究方向为水工结构安全与健康诊断,E-mail:
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曹梦茜(1998-),女,硕士研究生,研究方向为水工结构安全监测,E-mail:

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曹梦茜(1998-),女,硕士研究生,研究方向为水工结构安全监测,E-mail:

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基于EEMD-AEFA-LSTM的混凝土坝变形预测模型
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曹梦茜 a, b , 郑东健 a, b
水电能源科学 | 大坝安全与监测 2023,41(9): 89-93
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水电能源科学 | 大坝安全与监测 2023, 41(9): 89-93
基于EEMD-AEFA-LSTM的混凝土坝变形预测模型
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曹梦茜a, b , 郑东健a, b
作者信息
  • a.河海大学水利水电学院,江苏 南京 210098
  • b.河海大学水文水资源与水利工程科学国家重点实验室,江苏 南京 210098
  • 曹梦茜(1998-),女,硕士研究生,研究方向为水工结构安全监测,E-mail:

通讯作者:

郑东健(1965-),男,博士、教授、博导,研究方向为水工结构安全与健康诊断,E-mail:
Deformation Prediction Model of Concrete Dam Based on EEMD-AEFA-LSTM
Meng-xi CAOa, b , Dong-jian ZHENGa, b
Affiliations
  • a.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
  • b.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
出版时间: 2023-09-25 doi: 10.20040/j.cnki.1000-7709.2023.20222203
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对混凝土坝变形监测资料进行合理的数据挖掘和准确的预测分析是确保大坝安全长效运行的关键手段,大坝变形时间序列受到温度、水位等环境量的影响,具有周期性、非线性等变化特征,现有的智能算法无法很好地捕捉序列的非线性关系。对此,提出了基于EEMD-AEFA-LSTM模型的混凝土坝变形预测模型,采用集合经验模态分解EEMD有效分解变形时间序列,通过人工电场算法AEFA优化的长短期记忆网络LSTM模型对各分解分量进行预测并重构预测结果。选取某混凝土坝EX16、EX24测点的变形监测资料开展预测研究。结果表明,所建EEMD-AEFA-LSTM模型的预测精度明显高于AEFA-LSTM、PSO-LSTM、GA-LSTM模型,预测结果的平均绝对误差、均方误差、均方根误差均为最小值,为混凝土坝变形的精确预测提供了新途径。

集合经验模态分解  /  人工电场算法  /  长短期记忆网络  /  混凝土坝  /  变形预测

Reasonable data mining and accurate prediction and analysis of concrete dam deformation monitoring data are the key means to ensure the safe and long-term operation of the dam. Due to the impact of environmental variables such as temperature and water level, the dam deformation time series has periodic, nonlinear and other change characteristics. Existing intelligent algorithms can not capture the nonlinear relationship of sequences well, A concrete dam deformation prediction model based on EEMD-AEFA-LSTM model was proposed. Ensemble empirical mode decomposition was used to effectively decompose the deformation time series. The long short-term memory network model optimized by the artificial electric field algorithm was used to predict the decomposition components and reconstruct the prediction results. The deformation monitoring data of EX16 and EX24 measuring points of a concrete dam were selected for prediction research. The results show that the prediction accuracy of the EEMD-AEFA-LSTM model is significantly higher than that of the AEFA-LSTM model, PSO-LSTM model, and GA-LSTM model. The average absolute error, mean square error, and root mean square error of the prediction results are the minimum values, providing a new way for accurate prediction of concrete dam deformation.

ensemble empirical mode decomposition  /  artificial electric field algorithm  /  long short-term memory network  /  concrete dam  /  deformation prediction
曹梦茜, 郑东健. 基于EEMD-AEFA-LSTM的混凝土坝变形预测模型. 水电能源科学, 2023 , 41 (9) : 89 -93 . DOI: 10.20040/j.cnki.1000-7709.2023.20222203
Meng-xi CAO, Dong-jian ZHENG. Deformation Prediction Model of Concrete Dam Based on EEMD-AEFA-LSTM[J]. Water Resources and Power, 2023 , 41 (9) : 89 -93 . DOI: 10.20040/j.cnki.1000-7709.2023.20222203
  • 国家自然科学基金项目(52179128)
2023年第41卷第9期
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doi: 10.20040/j.cnki.1000-7709.2023.20222203
  • 接收时间:2022-10-20
  • 首发时间:2026-01-28
  • 出版时间:2023-09-25
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  • 收稿日期:2022-10-20
  • 修回日期:2022-11-15
基金
国家自然科学基金项目(52179128)
作者信息
    a.河海大学水利水电学院,江苏 南京 210098
    b.河海大学水文水资源与水利工程科学国家重点实验室,江苏 南京 210098

通讯作者:

郑东健(1965-),男,博士、教授、博导,研究方向为水工结构安全与健康诊断,E-mail:
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2种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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