大跨度空间网格结构的损伤定位
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摘要
本文建立了基于模态曲率法和人工神经网络技术相结合的、适用于大跨度空间网格结构的损伤定位新方法,即首先应用模态曲率法判断结构是否发生损伤并识别发生损伤的局部结构,然后对发生损伤的局部结构利用人工神经网络技术识别损伤的准确位置。通过分析和比较发现,以模态曲率为基础的损伤参数比较适合于大跨度空间网格结构的损伤定位,三种以模态曲率为基础的损伤定位参数按有效性进行排序,从低到高依次为模态曲率、模态曲率差、模态曲率变化率;针对天津奥林匹克中心体育场大跨度悬挑管桁结构进行了不同损伤状况的数值模拟,验证了所建立的损伤定位方法的适用性和有效性。研究结果表明:利用模态曲率变化率识别损伤发生的大致位置,当单榀桁架发生损伤时,识别的准确率达到100%,当多榀桁架同时发生损伤时,识别的准确率达93.7%;采用人工神经网络技术识别损伤桁架的准确损伤位置时,在无测量噪声影响下,损伤定位的准确率达到97.0%,且测量噪声对损伤定位准确率的影响很大。
In this paper,based on the combination of the mode curvature method and the artificial neural network technology,a new method of damage localization for the long-span spatial lattice structures is established,in which the decision of whether damage will occur or not and the identification of damaged partial structure are to be carried out by using the mode curvature method firstly,and then the accurate damage location in the damaged partial structure is recognized using the artificial neural network technology.Through the investigation and comparison,the parameters suitable for the damage localization of the long-span spatial lattice structures are determined,and the different damage cases for a long-span cantilever tubular truss structure of the Tianjin Olympic Center Stadium is numerically simulated to verify the applicability and effectiveness of the established damage localization method.The results show that during the approximate damage location is identified using the varying ratio of mode curvature method,the identification accuracy can reach 100% when the damage occur only in truss,and 93.7% when the damage occur in multiple trusses simultaneously.While the accurate damage location of the damaged truss is identified using the artificial neural network technology,the accuracy for damage location can reach 97.0% in the case of without test noise,it indicates that the influence of test noise on the accuracy of damage location is significant.
In this paper,based on the combination of the mode curvature method and the artificial neural network technology,a new method of damage localization for the long-span spatial lattice structures is established,in which the decision of whether damage will occur or not and the identification of damaged partial structure are to be carried out by using the mode curvature method firstly,and then the accurate damage location in the damaged partial structure is recognized using the artificial neural network technology.Through the investigation and comparison,the parameters suitable for the damage localization of the long-span spatial lattice structures are determined,and the different damage cases for a long-span cantilever tubular truss structure of the Tianjin Olympic Center Stadium is numerically simulated to verify the applicability and effectiveness of the established damage localization method.The results show that during the approximate damage location is identified using the varying ratio of mode curvature method,the identification accuracy can reach 100% when the damage occur only in truss,and 93.7% when the damage occur in multiple trusses simultaneously.While the accurate damage location of the damaged truss is identified using the artificial neural network technology,the accuracy for damage location can reach 97.0% in the case of without test noise,it indicates that the influence of test noise on the accuracy of damage location is significant.
引文
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[11]Pandey A K,Biswas M,Samman M M.Damage detection fromchanges in curvature mode shapes[J].Journal of Sound andVibration,1991,145(2):321-332.
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[14]姜绍飞.基于神经网络的结构优化与损伤检测[M].北京:科学出版社,2002.
[15]Elkordy M F,Chang K C,Lee G C.Application of neuralnetworks in vibrational signature analysis[J].Journal ofEngineering Mechanics,1994,120(2):251-264.
[16]Tsou P,Shen M H H.Structural damage detection and identifi-cation using neural network[J].American Institute ofAreonautics and Astronautics Journal,1994,32(1):176-183.
[17]《网架结构手册》编委会.网架结构手册[M].北京:中国建筑工业出版社,1998.
[18]Yun C B,Bahng E Y.Substructural identification using network[J].Computers&Structures,2000,77(1):41-52.
[19]张德文,魏阜旋.模型修正与破损诊断[M].北京:科学出版社,1999.
[2]Farrar C R,Jauregui D A.Comparative study of damage identifi-cation algorithms applied to a bridge:I.Experiment[J].SmartMaterials and Structures,1998,7(5):704-719.
[3]Farrar C R,Jauregui D A.Comparative study of damage identifi-cation algorithms applied to a bridge:II.Numerical study[J].Smart Materials and Structures,1998,7(5):720-731.
[4]Lam H F,Ko J M,Wong C W.Localization of damage structuralconnections based on experimental modal and sensitivity analysis[J].Journal of Sound and Vibration,1998,210(1):91-115.
[5]李国强,李杰.工程结构动力检测理论与应用[M].北京:科学出版社,2002.
[6]李国强,梁远森.振型曲率在板类结构动力检测中的应用[J].振动、测试与诊断,2004,24(2):111-116.
[7]高赞明,孙宗光,倪一清.基于振动方法的汲水门大桥损伤检测研究[J].地震工程与工程振动,2001,21(4):117-124.
[8]李功宇,郑华文.损伤结构的曲率模态分析[J].振动、测试与诊断,2002,22(2):136-141.
[9]易登军,韩晓林.损伤结构的实验曲率模态研究[J].振动、测试与诊断,2004,24(3):234-237.
[10]陈素文,李国强.人工神经网络在结构损伤识别中的应用[J].振动、测试与诊断,2001,21(2):116-123.
[11]Pandey A K,Biswas M,Samman M M.Damage detection fromchanges in curvature mode shapes[J].Journal of Sound andVibration,1991,145(2):321-332.
[12]Maeck J,Peeters B,De Roeck G.Damage identification on theZ24 bridge using vibration monitoring[J].Smart Materials andStructures,2001,10(3):512-517.
[13]李德葆,陆秋海.实验模态分析及其应用[M].北京:科学出版社,2001.
[14]姜绍飞.基于神经网络的结构优化与损伤检测[M].北京:科学出版社,2002.
[15]Elkordy M F,Chang K C,Lee G C.Application of neuralnetworks in vibrational signature analysis[J].Journal ofEngineering Mechanics,1994,120(2):251-264.
[16]Tsou P,Shen M H H.Structural damage detection and identifi-cation using neural network[J].American Institute ofAreonautics and Astronautics Journal,1994,32(1):176-183.
[17]《网架结构手册》编委会.网架结构手册[M].北京:中国建筑工业出版社,1998.
[18]Yun C B,Bahng E Y.Substructural identification using network[J].Computers&Structures,2000,77(1):41-52.
[19]张德文,魏阜旋.模型修正与破损诊断[M].北京:科学出版社,1999.
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