An identification approach for linear and nonlinear time-variant structural systems via harmonic wavelets

2013 ◽  
Vol 37 (1-2) ◽  
pp. 338-352 ◽  
Author(s):  
Ioannis A. Kougioumtzoglou ◽  
Pol D. Spanos
Keyword(s):  
Structural Systems ◽  
Harmonic Wavelets ◽  
Identification Approach ◽  
Linear And Nonlinear

On-Line Identification of Hysteretic Systems

1998 ◽  
Vol 65 (1) ◽  
pp. 194-203 ◽  
Author(s):  
A. G. Chassiakos ◽  
S. F. Masri ◽  
A. W. Smyth ◽  
T. K. Caughey
Keyword(s):  
Adaptive Estimation ◽  
Structural Systems ◽  
Simulation Studies ◽  
Restoring Force ◽  
Hysteretic Systems ◽  
Line Identification ◽  
Wide Range ◽  
On Line ◽  
Identification Approach ◽  
Hereditary Nature

Using adaptive estimation approaches, a method is presented for the on-line identification of hysteretic systems under arbitrary dynamic environments. The availability of such an identification approach is crucial for the on-line control and monitoring of nonlinear structural systems to be actively controlled. In spite of the challenges encountered in the identification of the hereditary nature of the restoring force of such nonlinear systems, it is shown through the use of simulation studies and experimental measurements that the proposed approach can yield reliable estimates of the hysteretic restoring force under a very wide range of excitation levels and response ranges.

Order Reduction of Parametrically Excited Linear and Nonlinear Structural Systems

2005 ◽  
Vol 128 (4) ◽  
pp. 458-468 ◽  
Author(s):  
Venkatesh Deshmukh ◽  
Eric A. Butcher ◽  
S. C. Sinha
Keyword(s):  
Nonlinear Systems ◽  
Order Reduction ◽  
Second Order ◽  
Degree Of Freedom ◽  
Structural Systems ◽  
Parametrically Excited ◽  
Reduced Models ◽  
Linear And Nonlinear ◽  
Time Invariant ◽  
Time Periodic

Order reduction of parametrically excited linear and nonlinear structural systems represented by a set of second order equations is considered. First, the system is converted into a second order system with time invariant linear system matrices and (for nonlinear systems) periodically modulated nonlinearities via the Lyapunov-Floquet transformation. Then a master-slave separation of degrees of freedom is used and a relation between the slave coordinates and the master coordinates is constructed. Two possible order reduction techniques are suggested. In the first approach a constant Guyan-like linear kernel which accounts for both stiffness and inertia is employed with a possible periodically modulated nonlinear part for nonlinear systems. The second method for nonlinear systems reduces to finding a time-periodic nonlinear invariant manifold relation in the modal coordinates. In the process, closed form expressions for “true internal” and “true combination” resonances are obtained for various nonlinearities which are generalizations of those previously reported for time-invariant systems. No limits are placed on the size of the time-periodic terms thus making this method extremely general even for strongly excited systems. A four degree-of-freedom mass- spring-damper system with periodic stiffness and damping as well as two and five degree-of-freedom inverted pendula with periodic follower forces are used as illustrative examples. The nonlinear-based reduced models are compared with linear-based reduced models in the presence and absence of nonlinear resonances.

Linear and nonlinear pounding of structural systems

1998 ◽  
Vol 66 (1) ◽  
pp. 79-92 ◽  
Author(s):  
C.P. Pantelides ◽  
X. Ma
Keyword(s):  
Structural Systems ◽  
Linear And Nonlinear

scholarly journals Unified Probabilistic Approach for Model Updating and Damage Detection

2005 ◽  
Vol 73 (4) ◽  
pp. 555-564 ◽  
Author(s):  
Ka-Veng Yuen ◽  
James L. Beck ◽  
Lambros S. Katafygiotis
Keyword(s):  
Damage Detection ◽  
Model Updating ◽  
Probabilistic Approach ◽  
Structural Response ◽  
Structural Systems ◽  
Ambient Vibrations ◽  
Single Degree Of Freedom ◽  
Stiffness Reduction ◽  
Identification Approach ◽  
Story Building

A probabilistic approach for model updating and damage detection of structural systems is presented using noisy incomplete input and incomplete response measurements. The situation of incomplete input measurements may be encountered, for example, during low-level ambient vibrations when a structure is instrumented with accelerometers that measure the input ground motion and the structural response at a few instrumented locations but where other excitations, e.g., due to wind, are not measured. The method is an extension of a Bayesian system identification approach developed by the authors. A substructuring approach is used for the parameterization of the mass, damping and stiffness distributions. Damage in a substructure is defined as stiffness reduction established through the observation of a reduction in the values of the various substructure stiffness parameters compared with their initial values corresponding to the undamaged structure. By using the proposed probabilistic methodology, the probability of various damage levels in each substructure can be calculated based on the available dynamic data. Examples using a single-degree-of-freedom oscillator and a 15-story building are considered to demonstrate the proposed approach.

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