Article(id=1223204296221643223, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20220912, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1651593600000, receivedDateStr=2022-05-04, revisedDate=1659542400000, revisedDateStr=2022-08-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1769564230255, onlineDateStr=2026-01-28, pubDate=1684944000000, pubDateStr=2023-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769564230255, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769564230255, creator=13701087609, updateTime=1769564230255, updator=13701087609, issue=Issue{id=1223204286050452333, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='5', 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=1769564227831, creator=13701087609, updateTime=1769567742010, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223219026013323264, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223219026013323265, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=67, endPage=70, ext={EN=ArticleExt(id=1223204296964035046, articleId=1223204296221643223, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Inversion of Permeability Coefficient for Concrete Face Rockfill Dam Based on PSO-GRNN and Its Application, columnId=1222925280734335368, journalTitle=Water Resources and Power, columnName=DAM SAFETY AND MONITORING, runingTitle=null, highlight=null, articleAbstract=

Considering the characteristics of inversion problems of concrete face dam, including high dimensionality, complex calculation and excessive calculation time, the orthogonal experimental design was used to construct the learning sample composed of the combination of permeability coefficient and the water head of pressure measuring point. The nonlinear mapping relationship between the water head at monitoring points and permeability coefficient was established by general regression neural network (GRNN), and the particle swarm optimization (PSO) algorithm was used to search for the smoothing factor σ suitable for the specific project to improve the generalization and convergence speed of the model. The PSO-GRNN model for the inversion of the permeability coefficient of concrete face dam was established, and was applied on an engineering example. The results show that the value of permeability coefficient obtained by inversion based on the model is reasonable, and the maximum relative error between the calculated value of water head at monitoring points obtained by seepage analysis and the measured value is 3.64%, and the accuracy meets the needs of engineering.

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针对混凝土面板坝渗透系数反演维数高、计算复杂、耗时长的问题,采用正交试验设计构建渗透系数组合与测压点水头组成的学习样本,通过广义回归神经网络(GRNN)建立渗压监测点水头与渗透系数之间的非线性映射关系,并引入粒子群优化算法(PSO)搜寻适合特定工程的光滑因子σ值,提高模型的泛化性和收敛速度,建立了混凝土面板坝渗透系数反演的PSO-GRNN模型,并应用于工程实例。结果表明,基于该模型反演得到的渗透系数取值合理,渗流分析得到的渗压监测点水头与实测值相对误差最大为3.64%,精度满足工程需要。

, correspAuthors=null, authorNote=null, correspAuthorsNote=
沈振中(1968-),男,教授、博导,研究方向为工程渗流特性分析控制,E-mail:
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李皓璇(1999-),男,硕士研究生,研究方向为水利工程渗流特性分析控制,E-mail:

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李皓璇(1999-),男,硕士研究生,研究方向为水利工程渗流特性分析控制,E-mail:

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李皓璇(1999-),男,硕士研究生,研究方向为水利工程渗流特性分析控制,E-mail:

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基于PSO-GRNN的混凝土面板堆石坝渗透系数反演方法及应用
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李皓璇 1 , 沈振中 1 , 张文兵 1, 2
水电能源科学 | 大坝安全与监测 2023,41(5): 67-70
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水电能源科学 | 大坝安全与监测 2023, 41(5): 67-70
基于PSO-GRNN的混凝土面板堆石坝渗透系数反演方法及应用
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李皓璇1 , 沈振中1 , 张文兵1, 2
作者信息
  • 1.河海大学水利水电学院,江苏 南京 210098
  • 2.上海海事大学海洋科学与工程学院,上海 201306
  • 李皓璇(1999-),男,硕士研究生,研究方向为水利工程渗流特性分析控制,E-mail:

通讯作者:

沈振中(1968-),男,教授、博导,研究方向为工程渗流特性分析控制,E-mail:
Inversion of Permeability Coefficient for Concrete Face Rockfill Dam Based on PSO-GRNN and Its Application
Hao-xuan LI1 , Zhen-zhong SHEN1 , Wen-bing ZHANG1, 2
Affiliations
  • 1.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
  • 2.College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
出版时间: 2023-05-25 doi: 10.20040/j.cnki.1000-7709.2023.20220912
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针对混凝土面板坝渗透系数反演维数高、计算复杂、耗时长的问题,采用正交试验设计构建渗透系数组合与测压点水头组成的学习样本,通过广义回归神经网络(GRNN)建立渗压监测点水头与渗透系数之间的非线性映射关系,并引入粒子群优化算法(PSO)搜寻适合特定工程的光滑因子σ值,提高模型的泛化性和收敛速度,建立了混凝土面板坝渗透系数反演的PSO-GRNN模型,并应用于工程实例。结果表明,基于该模型反演得到的渗透系数取值合理,渗流分析得到的渗压监测点水头与实测值相对误差最大为3.64%,精度满足工程需要。

混凝土面板坝  /  渗透系数  /  反演分析  /  广义回归神经网络  /  粒子群算法

Considering the characteristics of inversion problems of concrete face dam, including high dimensionality, complex calculation and excessive calculation time, the orthogonal experimental design was used to construct the learning sample composed of the combination of permeability coefficient and the water head of pressure measuring point. The nonlinear mapping relationship between the water head at monitoring points and permeability coefficient was established by general regression neural network (GRNN), and the particle swarm optimization (PSO) algorithm was used to search for the smoothing factor σ suitable for the specific project to improve the generalization and convergence speed of the model. The PSO-GRNN model for the inversion of the permeability coefficient of concrete face dam was established, and was applied on an engineering example. The results show that the value of permeability coefficient obtained by inversion based on the model is reasonable, and the maximum relative error between the calculated value of water head at monitoring points obtained by seepage analysis and the measured value is 3.64%, and the accuracy meets the needs of engineering.

concrete face dam  /  permeability coefficient  /  inversion analysis  /  GRNN  /  PSO
李皓璇, 沈振中, 张文兵. 基于PSO-GRNN的混凝土面板堆石坝渗透系数反演方法及应用. 水电能源科学, 2023 , 41 (5) : 67 -70 . DOI: 10.20040/j.cnki.1000-7709.2023.20220912
Hao-xuan LI, Zhen-zhong SHEN, Wen-bing ZHANG. Inversion of Permeability Coefficient for Concrete Face Rockfill Dam Based on PSO-GRNN and Its Application[J]. Water Resources and Power, 2023 , 41 (5) : 67 -70 . DOI: 10.20040/j.cnki.1000-7709.2023.20220912
  • 国家自然科学基金项目(52179130)
  • 中央高校基本科研业务费专项资金项目(B210203065)
2023年第41卷第5期
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doi: 10.20040/j.cnki.1000-7709.2023.20220912
  • 接收时间:2022-05-04
  • 首发时间:2026-01-28
  • 出版时间:2023-05-25
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  • 收稿日期:2022-05-04
  • 修回日期:2022-08-04
基金
国家自然科学基金项目(52179130)
中央高校基本科研业务费专项资金项目(B210203065)
作者信息
    1.河海大学水利水电学院,江苏 南京 210098
    2.上海海事大学海洋科学与工程学院,上海 201306

通讯作者:

沈振中(1968-),男,教授、博导,研究方向为工程渗流特性分析控制,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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