基于结构动力参数的RBF损伤辨识方法
|
摘要
本文研究了基于不同结构动力参数的径向基网络损伤辨识方法,并结合简支梁损伤辨识进行了应用。研究发现:(1)径向基网络的输入参数选择对结果有较大影响;(2)使用模态曲率变化作为输入参数的网络辨识效果优于采用频率变化率的辨识效果;(3)综合使用频率变化率和模态曲率变化的网络辨识效果优于单独使用频率变化率或模态曲率的效果。结果表明,基于动力参数和径向基神经网络的结构损伤辨识方法能够准确地辨识结构损伤。
RBF damage identification based on different structural dynamic parameters is studied in this paper. The method is applied in the damage detection for the simply supported beam.The study reveals that:the input parameters of RBF networks have bigger influence for the identification effect;taking the change of mode shape curvature、as input parameter,the identification effect is better than taking the change of frequency as input parameter;further more,the combination of the frequency change and change of mode shape curvature gives the best results.The research proved that the structural damage can be identified by using RBF networks and dynamic parameters.
RBF damage identification based on different structural dynamic parameters is studied in this paper. The method is applied in the damage detection for the simply supported beam.The study reveals that:the input parameters of RBF networks have bigger influence for the identification effect;taking the change of mode shape curvature、as input parameter,the identification effect is better than taking the change of frequency as input parameter;further more,the combination of the frequency change and change of mode shape curvature gives the best results.The research proved that the structural damage can be identified by using RBF networks and dynamic parameters.
引文
[1]Doebling SW,Farrar CR,Prime MB,al.et.Damage Identification and Health Monitoring of Structural and Mechanical Systems From Changes in Their Vibration Characteristics:A Literature Review[R].Los Alamos, New Mexico:Los Alamos National Laboratory,1996
[2]Sohn H,CR Farrar,Hemez FM et al.A review of structural health monitoring literature:1996-2001[R].Los Alamos,New Mexico:Los Alamos National Laboratory,2004
[3]Wu X.,Ghaboussi J.,Garrett J H.Use of neural networks in detection of structural damage[J].Computers＆Structures,1992,42(4):649 - 659
[4]Elkordy M F,Chang K C,Lee G C.Application of neural networks in vibrational signature analysis[J].Journal of Engineering Mechanics, 1994,120(2):251 - 264
[5]Pandey PC,Barai,SV.Multilayer perceptron in damage detection of bridge structures[J].Computers＆Structures,1995,54(4):597 - 608
[6]Choi M.Y.,Kwon I.B.Damage detection system of a real steel truss bridge by neural networks[J].Smart Structures and Materials 2000: Smart Systems for Bridges,Structures,and Highways,2000,3988:295 -306
[7]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,2003,23(03): 92-99
[8]杨杰,李爱群,缪长青.BP神经网络在大跨斜拉桥的斜拉索损伤识别中的应用[J].土木工程学报,2006,39(5):72-77,95
[9]胡良红,刘效尧.基于改进的BP神经网络的钢桁梁桥损伤识别[J].合肥工业大学学报(自然科学版),2006,29(05):576- 57q
[10]Broomhead DS,Lowe D.Multivariable functional interpolation and adaptive networks[J].Complex Systems,1988,2:321 - 355
[2]Sohn H,CR Farrar,Hemez FM et al.A review of structural health monitoring literature:1996-2001[R].Los Alamos,New Mexico:Los Alamos National Laboratory,2004
[3]Wu X.,Ghaboussi J.,Garrett J H.Use of neural networks in detection of structural damage[J].Computers＆Structures,1992,42(4):649 - 659
[4]Elkordy M F,Chang K C,Lee G C.Application of neural networks in vibrational signature analysis[J].Journal of Engineering Mechanics, 1994,120(2):251 - 264
[5]Pandey PC,Barai,SV.Multilayer perceptron in damage detection of bridge structures[J].Computers＆Structures,1995,54(4):597 - 608
[6]Choi M.Y.,Kwon I.B.Damage detection system of a real steel truss bridge by neural networks[J].Smart Structures and Materials 2000: Smart Systems for Bridges,Structures,and Highways,2000,3988:295 -306
[7]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,2003,23(03): 92-99
[8]杨杰,李爱群,缪长青.BP神经网络在大跨斜拉桥的斜拉索损伤识别中的应用[J].土木工程学报,2006,39(5):72-77,95
[9]胡良红,刘效尧.基于改进的BP神经网络的钢桁梁桥损伤识别[J].合肥工业大学学报(自然科学版),2006,29(05):576- 57q
[10]Broomhead DS,Lowe D.Multivariable functional interpolation and adaptive networks[J].Complex Systems,1988,2:321 - 355
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心