基于人工神经网络的混合梁斜拉桥智能诊断方法研究
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摘要
研究目的:本文以天津市河北大街混合梁斜拉桥为工程背景,基于人工神经网络模型,提出适用于混合梁斜拉桥的分步识别方法,分别采用概率和径向基函数神经网络对子结构和钢主梁子结构局部构件进行损伤识别。此外还提出适用于钢主梁局部构件识别的动-静组合损伤指标,并建立相应的径向基函数网络模型,分别针对单损伤、双损伤和三损伤的不同损伤情况进行数值模拟。研究结论:识别结果表明:(1)本文所提出的分步识别方法具有较高的识别精度,网络识别速度快,适用于大型混合梁斜拉桥的智能诊断过程;(2)所提出的动-静组合损伤指标对混合梁斜拉桥的局部损伤识别也较为敏感;(3)单处损伤测试工况中,识别精度几乎高达100%;(4)在两处和三处损伤测试工况中,位置识别正确率分别达到82.61%和78.3%。
Research purposes: It has significant engineering value and research meaning to do research on intelligent diagnosis methods of a hybrid girder cable-stayed bridge.Taking Tianjin Hebei Street hybrid girder cable-stayed bridge as the engineering background,based on artificial neural networks,the method of hierarchical damage identification which is suitable to hybrid girder cable-stayed bridge is presented: the damaged substructure and damaged steel girder substructural components can be detected by using Probablistic Neural Network(PNN) and Radial Basis Function(RBF) Neural Network individually.Furthermore,an combined static and dynamic damage sensitive index which is suitable in the second step is presented,a RBF Neural Network model is constituted and used to simulate three damage conditions,i.e.single damage and double or three damages which occurred simultaneously. Research conclusions:The identification results show that:(1) The proposed hierarchical damage identification method has a identified precision and efficiency,it is suitable to intelligent diagnosis process of hybrid girder cable-stayed bridges.(2) The combined static-dynamic damage identification index is also sensitive to cable-stayed hybrid girder bridges.(3) The identified precision for single damage cases is nearly to 100%.(4) For double and triple damage cases individually,the identified precision is nearly to 82.61% and 78.3%.
Research purposes: It has significant engineering value and research meaning to do research on intelligent diagnosis methods of a hybrid girder cable-stayed bridge.Taking Tianjin Hebei Street hybrid girder cable-stayed bridge as the engineering background,based on artificial neural networks,the method of hierarchical damage identification which is suitable to hybrid girder cable-stayed bridge is presented: the damaged substructure and damaged steel girder substructural components can be detected by using Probablistic Neural Network(PNN) and Radial Basis Function(RBF) Neural Network individually.Furthermore,an combined static and dynamic damage sensitive index which is suitable in the second step is presented,a RBF Neural Network model is constituted and used to simulate three damage conditions,i.e.single damage and double or three damages which occurred simultaneously. Research conclusions:The identification results show that:(1) The proposed hierarchical damage identification method has a identified precision and efficiency,it is suitable to intelligent diagnosis process of hybrid girder cable-stayed bridges.(2) The combined static-dynamic damage identification index is also sensitive to cable-stayed hybrid girder bridges.(3) The identified precision for single damage cases is nearly to 100%.(4) For double and triple damage cases individually,the identified precision is nearly to 82.61% and 78.3%.
引文
[1]刘琼,黄彩萍,党志杰,等.斜拉桥箱梁钢-混结合段受力的试验研究[J].铁道工程学报,2010(9):46-49.Liu Qiong,Huang Caiping,Dang Zhijie,etc.Experimental Research on the Load CarryingPerformance of Steel-concrete Junction in Box Girderof Cable-stayed Bridge[J].Journal of RailwayEngineering Society,2010(9):46-49.
[2]李忠献,杨晓明,丁阳.应用人工神经网络技术的大型斜拉桥子结构损伤识别研究[J].地震工程与工程振动,2004(3):92-99.Li Zhongxian,Yang Xiaoming,DingYang.Research onSubstructural Damage Identification of Large Cable-stayed Bridges using Artificial Neural Networks[J].Earthquake Engineering and Engineering Vibration,2003(3):93-99.
[3]禚一.大跨混合梁斜拉桥健康监测系统及智能诊断方法研究识别研究[D].天津:天津大学,2007.Zhuo Yi.Health Monitoring System and IntelligentDiagnosis Methods of a Long-Span Cable-StayedHybrid Girder Bridge[D].Tianjin:Tianjin University,2007.
[4]Specht D F.Probabilistic neural networks[J].NeuralNetworks,1990(3):109-118.
[5]田景文,高美娟.人工神经网络算法研究及应用[M].北京:北京理工大学出版社,2006.Tian Jingwen,Gao Meijuan.Research and Applicationof Artificial Neural Network Algorithm[M].Beijing:Beijing Institute of Technology Press,2006.
[6]范立础.桥梁抗震[M].上海:同济大学出版社,1997.Fan Lichu.Seismic Design of Bridges[M].Shanghai:Tongji Univerisity Press,1996.
[7]Cawley P,Adams R D.The Location of Defects inStructures From Measurements of Natural Frequencies[J].Journal of Strain Analysis,1979(2):49-57.
[8]Kaminski P C.The Approximate Location of DamageThrough the Analysis of Natural Frequencies withArtificial Neural Networks[J].Journal of ProcessMechanical Engineering,1995(209):117-123.
[9]朱劲松,肖汝诚.大跨度PC斜拉桥结构快速分析的神经网络模型研究[J].中国铁道科学,2007(1):58-64.Zhu jinsong,Xiao Rucheng.Research on NeuralNetwork Model to Structural Simulation of Large-spanPC Cable-stayed Bridges[J].China Railway Science,2007(1):58-64.
[2]李忠献,杨晓明,丁阳.应用人工神经网络技术的大型斜拉桥子结构损伤识别研究[J].地震工程与工程振动,2004(3):92-99.Li Zhongxian,Yang Xiaoming,DingYang.Research onSubstructural Damage Identification of Large Cable-stayed Bridges using Artificial Neural Networks[J].Earthquake Engineering and Engineering Vibration,2003(3):93-99.
[3]禚一.大跨混合梁斜拉桥健康监测系统及智能诊断方法研究识别研究[D].天津:天津大学,2007.Zhuo Yi.Health Monitoring System and IntelligentDiagnosis Methods of a Long-Span Cable-StayedHybrid Girder Bridge[D].Tianjin:Tianjin University,2007.
[4]Specht D F.Probabilistic neural networks[J].NeuralNetworks,1990(3):109-118.
[5]田景文,高美娟.人工神经网络算法研究及应用[M].北京:北京理工大学出版社,2006.Tian Jingwen,Gao Meijuan.Research and Applicationof Artificial Neural Network Algorithm[M].Beijing:Beijing Institute of Technology Press,2006.
[6]范立础.桥梁抗震[M].上海:同济大学出版社,1997.Fan Lichu.Seismic Design of Bridges[M].Shanghai:Tongji Univerisity Press,1996.
[7]Cawley P,Adams R D.The Location of Defects inStructures From Measurements of Natural Frequencies[J].Journal of Strain Analysis,1979(2):49-57.
[8]Kaminski P C.The Approximate Location of DamageThrough the Analysis of Natural Frequencies withArtificial Neural Networks[J].Journal of ProcessMechanical Engineering,1995(209):117-123.
[9]朱劲松,肖汝诚.大跨度PC斜拉桥结构快速分析的神经网络模型研究[J].中国铁道科学,2007(1):58-64.Zhu jinsong,Xiao Rucheng.Research on NeuralNetwork Model to Structural Simulation of Large-spanPC Cable-stayed Bridges[J].China Railway Science,2007(1):58-64.
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