Parametric Identification of Structures with Nonlinearities Using Global and Substructure Approaches in the Time Domain

Abstract

This paper presents further research on the parametric identification of structures with non-linearities in stiffness and damping properties. Parametric identification is carried out using acceleration responses in the time domain and is useful for structural health monitoring. Cubic nonlinearities in springs and quadratic nonlinearities in dampers are considered. Structural parametric identification is modeled as an inverse problem, based on minimizing the difference between measured responses and calculated responses from a mathematical model. The results of both global and substructural identification approaches are compared. The substructural approach allows us to identify a smaller domain while ignoring external parameters, resulting in a reduced model, but on the other hand the formulation is more complex. Genetic algorithms (GA) are used for filtering the unknown parameter values from within a given range. Simple real coded GA as well as a superior hybrid version obtained by combining with the Levenberg-Marquardt (LM) have been studied. Several numerical examples, including variations of a 10 DOF non-linear lumped mass system and a 12 member truss with several non-linear tuned mass dampers have been studied. The effect of measurement noise have been considered. The substructural method is shown to be superior overall in terms of speed, accuracy and economy (number of sensors) although the global identification approach implemented in conjunction with hybrid GA performs well in some cases.

Parametric Identification of Nonlinear Structures Using Particle Swarm Optimization Based on Power Flow Balance Criteria

“…The substructure nonlinear parameter identification is explained through a numerical model of 10DOF lumped mass model in Kumar and Shankar [11] was selected with different excitation load conditions. The global and substructure considerations are as shown in Fig.…”

“…Kapaniya [10] introduced a two-step identification process using Time Finite element Method (TFM) for the structural parameter identification of both linear and nonlinear terms separately. Kumar and Shankar [11] worked on structural parametric identification with cubic nonlinearity in springs and quadratic nonlinearities in dampers. They treated the problem as inverse using substructure acceleration matching objective function with Genetic Algorithms (GA) optimization search tool.…”

Identification of Tall Shear Buildings under Unknown Seismic Excitation with Limited Output Measurements

“…Moreover, as an inverse problem, direct identification of a large number of unknown parameters can lead to the problems of ill-condition and computational divergent. Consequently, substructural identification approaches are used, in which a large structure is decomposed into smaller size substructures with fewer unknown parameters (Koh et al 1991(Koh et al , 2003Yun and Bahng 2000;Lei et al 2007;Kumar et al 2009;Law et al 2011;Weng et al 2011;Xia et al 2011). The tall shear building is divided into m substructures shown in Figure 2(a).…”

A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

“…Physical parameters are essential for the dynamic modeling and analysis of road vehicles [1], but in practice they are very difficult to obtain. Therefore, methods to estimate these parameters are of great interest to researchers as well as to the industry [2][3][4][5]. Physical parameter identification is the second type of dynamical inverse problem.…”

“…In recent years, many time domain identification methods have been developed to identify the parameter of the onroad vehicle. Kumar and Shankar used global and substructure approaches in the time domain to identify the parameter of structures with nonlinearities [2]. The online parameter estimation method proposed by Rozyn and Zhang used the equivalent suspension stiffness coefficient to represent suspension and wheel stations in order to simplify modeling [3].…”