Detection and parametric identification of structural nonlinear restoring forces from partial measurements of structural responses

被引:8
作者
Lei, Ying [1 ]
Hua, Wei [1 ]
Luo, Sujuan [1 ]
He, Mingyu [1 ]
机构
[1] Xiamen Univ, Dept Civil Engn, Xiamen 361005, Peoples R China
关键词
nonlinear structural systems; parametric identification; nonlinear restoring force; extended Kalman filter; partial measurements; SYSTEM IDENTIFICATION; VIBRATING STRUCTURES; DAMAGE DETECTION; KALMAN FILTER; LIMITED INPUT; MODEL;
D O I
10.12989/sem.2015.54.2.291
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Compared with the identification of linear structures, it is more challenging to conduct identification of nonlinear structure systems, especially when the locations of structural nonlinearities are not clear in structural systems. Moreover, it is highly desirable to develop methods of parametric identification using partial measurements of structural responses for practical application. To cope with these issues, an identification method is proposed in this paper for the detection and parametric identification of structural nonlinear restoring forces using only partial measurements of structural responses. First, an equivalent linear structural system is proposed for a nonlinear structure and the locations of structural nonlinearities are detected. Then, the parameters of structural nonlinear restoring forces at the locations of identified structural nonlinearities together with the linear part structural parameters are identified by the extended Kalman filter. The proposed method simplifies the identification of nonlinear structures. Numerical examples of the identification of two nonlinear multi-story shear frames and a planar nonlinear truss with different nonlinear models and locations are used to validate the proposed method.
引用
收藏
页码:291 / 304
页数:14
相关论文
共 35 条
[1]   Parametric identification of nonlinearity in structural systems using describing function inversion [J].
Aykan, Murat ;
Ozguven, H. Nevzat .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2013, 40 (01) :356-376
[2]   Identifying and quantifying structural nonlinearities in engineering applications from measured frequency response functions [J].
Carrella, A. ;
Ewins, D. J. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2011, 25 (03) :1011-1027
[3]   SOLID FRICTION DAMPING OF MECHANICAL VIBRATIONS [J].
DAHL, PR .
AIAA JOURNAL, 1976, 14 (12) :1675-1682
[4]   Restoring force and dynamic loadings identification for a nonlinear chain-like structure with partially unknown excitations [J].
He, Jia ;
Xu, Bin ;
Masri, Sami F. .
NONLINEAR DYNAMICS, 2012, 69 (1-2) :231-245
[5]   STRUCTURAL IDENTIFICATION BY EXTENDED KALMAN FILTER [J].
HOSHIYA, M ;
SAITO, E .
JOURNAL OF ENGINEERING MECHANICS-ASCE, 1984, 110 (12) :1757-1770
[6]   Movement identification model of port container crane based on structural health monitoring system [J].
Kaloop, Mosbeh R. ;
Sayed, Mohamed A. ;
Kim, Dookie ;
Kim, Eunsung .
STRUCTURAL ENGINEERING AND MECHANICS, 2014, 50 (01) :105-119
[7]   Past, present and future of nonlinear system identification in structural dynamics [J].
Kerschen, G ;
Worden, K ;
Vakakis, AF ;
Golinval, JC .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2006, 20 (03) :505-592
[8]  
Kumar RK, 2007, INT J STRUCT STAB DY, V7, P715
[9]   Parametric Identification of Structures with Nonlinearities Using Global and Substructure Approaches in the Time Domain [J].
Kumar, R. Kishore ;
Shankar, K. .
ADVANCES IN STRUCTURAL ENGINEERING, 2009, 12 (02) :195-210
[10]   A global-local approach to nonlinear system identification: A review [J].
Lee, Y. S. ;
Vakakis, A. F. ;
McFarland, D. M. ;
Bergman, L. A. .
STRUCTURAL CONTROL & HEALTH MONITORING, 2010, 17 (07) :742-760