基于扩展卡尔曼滤波的框架梁柱节点地震损伤识别
|
摘要
目前关于框架结构损伤诊断的研究大部分都着眼于框架结构梁柱单元的损伤,而实践证明,地震荷载下框架结构在梁柱连接节点处更容易发生损伤。然而对于复杂结构,其有限元模型存在大量的自由度,进而需要大量的传感器,相应的计算量也会加大。为了解决这一困难,文章首先用静力凝聚的方法把有限元模型降阶,然后再跟扩展卡尔曼滤波损伤识别算法结合起来进行结构的损伤识别,用一个6层钢框架的振动台试验数据对该方法进行试验验证,该试验数据由我国台湾大学罗俊雄教授提供。基于试验数据结果表明,将静力凝聚跟扩展卡尔曼滤波算法结合起来的算法不仅能识别出地震激励下梁柱节点损伤的位置,还能定量的识别出节点损伤的程度。
Recent research for damage detection mainly focuses on beams and columns of structures under seismic excitation. While in practical applications,it has shown that the occurrence of damage in the beam-column joints more frequent. For complex structures,a great deal of degree of freedom should be taken into consideration when one establishes a finite element model. Thus,a large amount of accelerometers are needed,and additionally large amount of calculation should be involved. To solve this problem,at first the static condensation technique is brought in to reduce the order of finite element model,then the damage detection can be performed by applying the extended Kalman filter.The capability of the proposed method in detecting the damage of joint connections in structures will be further demonstrated by using a shake table experimental data of a six-story steel frame which is provided by professor ChinHsiung Loh,Taiwan University,China. The experimental results show that the proposed method is capable of identifying not only the damage locations but also the damage severities of joints under seismic excitation.
Recent research for damage detection mainly focuses on beams and columns of structures under seismic excitation. While in practical applications,it has shown that the occurrence of damage in the beam-column joints more frequent. For complex structures,a great deal of degree of freedom should be taken into consideration when one establishes a finite element model. Thus,a large amount of accelerometers are needed,and additionally large amount of calculation should be involved. To solve this problem,at first the static condensation technique is brought in to reduce the order of finite element model,then the damage detection can be performed by applying the extended Kalman filter.The capability of the proposed method in detecting the damage of joint connections in structures will be further demonstrated by using a shake table experimental data of a six-story steel frame which is provided by professor ChinHsiung Loh,Taiwan University,China. The experimental results show that the proposed method is capable of identifying not only the damage locations but also the damage severities of joints under seismic excitation.
引文
[1]Jaspart J P.General report:session on connections[J].Journal of Constructional Steel Research,2000,55:69-89
[2]Lefik M,Boso DP,Schrefler B A.Artificial neuralnetworks in numerical modelling of composites[J].Comput Meth Appl Mech Eng,2009,198:1785-1804
[3]Sundaresan M J,Schulz M J,Ghoshal A,et al.An artificial neural system for structural health monitoring[D].Greensboro:University of North Carolina at Greensboro,2001
[4]Katkhudat H N,Dwairi H M,Shatarat N.System identification of steel framed structures with semi-rigid connections[J].Structural Engineering ancl Mechanics,2010,34,(3):351-366
[5]Eurocode 3 Design of stecl strilckn.es,Part 1-8:Design of joints[S].2003
[6]Weng J H,Loh C H,Yang J N.Experimental study of damage detection by data-driven subspace identification and finite-element model updating[J].Journal of Structural Engineering,2009,135(12):1533-1544
[7]Overschee P,Moor B.Subspace identification for linear systems:theory implementation application[M].Kluwer:The Netherlands,1996:95-134
[8]Wu J R,Li Q S.Structural parameter identification and damage detection for a steel structure using a two-stage finite element model updating method[J].Journal of Constructional Steel Research 2006,62:231-239
[9]Xu Longhe,Li Zhongxian,Qian Jiaru.Test analysis of detection of damage to a complicated spatial model structure[J].Acta Mech.Sin.,2011,27(3):399-405
[10]Ching J,Beck J L.Bayesian analysis of the phase II IASCASCE structural health monitoring experimental benchmark data[J].J.Eng.Mech.,2004,130:1233-1244
[11]Jazwinski A H.Stochastic processes and filtering theory[M].New York:Academic Press,1970
[12]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
[13]Chopra A K.Dynamics of structures,theory and applications to earthquake engineering[M].New Jersey:Prentice-Hall Inc,2001
[2]Lefik M,Boso DP,Schrefler B A.Artificial neuralnetworks in numerical modelling of composites[J].Comput Meth Appl Mech Eng,2009,198:1785-1804
[3]Sundaresan M J,Schulz M J,Ghoshal A,et al.An artificial neural system for structural health monitoring[D].Greensboro:University of North Carolina at Greensboro,2001
[4]Katkhudat H N,Dwairi H M,Shatarat N.System identification of steel framed structures with semi-rigid connections[J].Structural Engineering ancl Mechanics,2010,34,(3):351-366
[5]Eurocode 3 Design of stecl strilckn.es,Part 1-8:Design of joints[S].2003
[6]Weng J H,Loh C H,Yang J N.Experimental study of damage detection by data-driven subspace identification and finite-element model updating[J].Journal of Structural Engineering,2009,135(12):1533-1544
[7]Overschee P,Moor B.Subspace identification for linear systems:theory implementation application[M].Kluwer:The Netherlands,1996:95-134
[8]Wu J R,Li Q S.Structural parameter identification and damage detection for a steel structure using a two-stage finite element model updating method[J].Journal of Constructional Steel Research 2006,62:231-239
[9]Xu Longhe,Li Zhongxian,Qian Jiaru.Test analysis of detection of damage to a complicated spatial model structure[J].Acta Mech.Sin.,2011,27(3):399-405
[10]Ching J,Beck J L.Bayesian analysis of the phase II IASCASCE structural health monitoring experimental benchmark data[J].J.Eng.Mech.,2004,130:1233-1244
[11]Jazwinski A H.Stochastic processes and filtering theory[M].New York:Academic Press,1970
[12]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
[13]Chopra A K.Dynamics of structures,theory and applications to earthquake engineering[M].New Jersey:Prentice-Hall Inc,2001
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