Article(id=1223278140370322162, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222486, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1669305600000, receivedDateStr=2022-11-25, revisedDate=1673971200000, revisedDateStr=2023-01-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1769581836072, onlineDateStr=2026-01-28, pubDate=1698163200000, pubDateStr=2023-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769581836072, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769581836072, creator=13701087609, updateTime=1769581836072, updator=13701087609, issue=Issue{id=1223278131956551924, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='10', pageStart='1', pageEnd='228', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769581834067, creator=13701087609, updateTime=1769584342407, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223288652869030422, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223288652869030423, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=96, endPage=99, ext={EN=ArticleExt(id=1223278140752003874, articleId=1223278140370322162, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Inversion Analysis of High Arch Dam Mechanical Parameters Based on Multi-objective Optimization Strategy and WOA-BPNN, columnId=1222925280734335368, journalTitle=Water Resources and Power, columnName=DAM SAFETY AND MONITORING, runingTitle=null, highlight=null, articleAbstract=

With the increase of the service life of the dam, inversion analysis of dam mechanical parameters based on the prototype monitoring data is necessary. At present, the dam inversion analysis model is still mainly based on the dam displacement monitoring data, which can only reflect the dam displacement law at the macroscopic level, while the change law of the strain field and stress field of the dam is ignored from the mesoscopic perspective. Therefore, a high arch dam mechanical parameter inversion model based on WOA-BPNN and MOEA/D was proposed, which consider both the dam displacement monitoring data and the dam strain monitoring data. The mechanical parameter inversion analysis of the arch dam was carried by the dam’s displacement data and strain data. The results show that the inversion parameter is more accurate and reasonable.

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随着大坝服役年限的增加,根据原型观测资料对坝体和坝基的力学参数进行反演十分必要,目前大坝反分析模型仍以坝体实测位移场监控模型为主,其只能反映宏观上坝体和坝基的位移变化规律,而忽视了细观上大坝应变场和应力场的变化规律。因此,通过同时考虑大坝位移监测数据及大坝应变监测数据,提出了一种基于MOEA/D和WOA-BPNN的高拱坝力学参数反演模型,并采用某拱坝坝体位移测点及应变测点,对拱坝坝体混凝土及基础岩体的力学参数进行了反演分析。结果表明,最终反演结果更加准确、合理。

, correspAuthors=null, authorNote=null, correspAuthorsNote=
郑东健(1965-),男,教授、博导,研究方向为水工结构安全监测,E-mail:
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刘欣航(1999-),男,博士研究生,研究方向为水工结构安全监测,E-mail:

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刘欣航(1999-),男,博士研究生,研究方向为水工结构安全监测,E-mail:

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刘欣航(1999-),男,博士研究生,研究方向为水工结构安全监测,E-mail:

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基于多目标优化的WOA-BPNN高拱坝变形模量反演分析
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刘欣航 1 , 娄一青 2 , 郑东健 1
水电能源科学 | 大坝安全与监测 2023,41(10): 96-99
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水电能源科学 | 大坝安全与监测 2023, 41(10): 96-99
基于多目标优化的WOA-BPNN高拱坝变形模量反演分析
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刘欣航1 , 娄一青2, 郑东健1
作者信息
  • 1.河海大学水利水电学院,江苏 南京 210098
  • 2.温州市水利电力勘测设计院有限公司,浙江 温州 325002
  • 刘欣航(1999-),男,博士研究生,研究方向为水工结构安全监测,E-mail:

通讯作者:

郑东健(1965-),男,教授、博导,研究方向为水工结构安全监测,E-mail:
Inversion Analysis of High Arch Dam Mechanical Parameters Based on Multi-objective Optimization Strategy and WOA-BPNN
Xin-hang LIU1 , Yi-qing LOU2, Dong-jian ZHENG1
Affiliations
  • 1.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
  • 2.Wenzhou Water Conservancy and Electric Power Survey and Design Assembly Company Ltd., Wenzhou 325002, China
出版时间: 2023-10-25 doi: 10.20040/j.cnki.1000-7709.2023.20222486
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随着大坝服役年限的增加,根据原型观测资料对坝体和坝基的力学参数进行反演十分必要,目前大坝反分析模型仍以坝体实测位移场监控模型为主,其只能反映宏观上坝体和坝基的位移变化规律,而忽视了细观上大坝应变场和应力场的变化规律。因此,通过同时考虑大坝位移监测数据及大坝应变监测数据,提出了一种基于MOEA/D和WOA-BPNN的高拱坝力学参数反演模型,并采用某拱坝坝体位移测点及应变测点,对拱坝坝体混凝土及基础岩体的力学参数进行了反演分析。结果表明,最终反演结果更加准确、合理。

高拱坝  /  参数反演  /  机器学习  /  多目标优化  /  有限元

With the increase of the service life of the dam, inversion analysis of dam mechanical parameters based on the prototype monitoring data is necessary. At present, the dam inversion analysis model is still mainly based on the dam displacement monitoring data, which can only reflect the dam displacement law at the macroscopic level, while the change law of the strain field and stress field of the dam is ignored from the mesoscopic perspective. Therefore, a high arch dam mechanical parameter inversion model based on WOA-BPNN and MOEA/D was proposed, which consider both the dam displacement monitoring data and the dam strain monitoring data. The mechanical parameter inversion analysis of the arch dam was carried by the dam’s displacement data and strain data. The results show that the inversion parameter is more accurate and reasonable.

high arch dam  /  parameter inversion  /  machine learning  /  multi-objective optimization  /  finite element
刘欣航, 娄一青, 郑东健. 基于多目标优化的WOA-BPNN高拱坝变形模量反演分析. 水电能源科学, 2023 , 41 (10) : 96 -99 . DOI: 10.20040/j.cnki.1000-7709.2023.20222486
Xin-hang LIU, Yi-qing LOU, Dong-jian ZHENG. Inversion Analysis of High Arch Dam Mechanical Parameters Based on Multi-objective Optimization Strategy and WOA-BPNN[J]. Water Resources and Power, 2023 , 41 (10) : 96 -99 . DOI: 10.20040/j.cnki.1000-7709.2023.20222486
  • 国家自然科学基金项目(52179128)
2023年第41卷第10期
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doi: 10.20040/j.cnki.1000-7709.2023.20222486
  • 接收时间:2022-11-25
  • 首发时间:2026-01-28
  • 出版时间:2023-10-25
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  • 收稿日期:2022-11-25
  • 修回日期:2023-01-18
基金
国家自然科学基金项目(52179128)
作者信息
    1.河海大学水利水电学院,江苏 南京 210098
    2.温州市水利电力勘测设计院有限公司,浙江 温州 325002

通讯作者:

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

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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