No 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.

No abstract

No abstract

This technical note presents the parametric identification of multi-degree-of-freedom nonlinear dynamic systems in the time domain using a combination of Levenberg-Marquardt (LM) method and Genetic Algorithm (GA). Here the crucial initial values to the LM algorithm are supplied by GA with a small population size. Two nonlinear systems are studied, the complex one having two nonlinear spring-damper pairs. The springs have cubic nonlinearity (Duffing oscillator) and dampers have quadratic nonlinearity. The effects of noise in the acceleration measurements and sensitivity analysis are also studied. The performance of combined GA and LM method is compared with pure LM and pure GA in terms of solution time, accuracy and number of iterations, and convergence and great improvement is observed. This method is found to be suitable for the identification of complex nonlinear systems, where the repeated solution of the numerically difficult equations over many generations requires enormous computational effort.