Nonlinear system identification of base-excited structures using an intelligent parameter varying (IPV) modeling approach

Soheil Saadat; Gregory D. Buckner; Tadatoshi Furukawa; Mohammad N. Noori

doi:10.1117/12.484066

1 August 2003 Nonlinear system identification of base-excited structures using an intelligent parameter varying (IPV) modeling approach

Soheil Saadat, Gregory D. Buckner, Tadatoshi Furukawa, Mohammad N. Noori

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Proceedings Volume 5049, Smart Structures and Materials 2003: Modeling, Signal Processing, and Control; (2003) https://doi.org/10.1117/12.484066
Event: Smart Structures and Materials, 2003, San Diego, California, United States

Abstract

Health monitoring and damage detection strategies for base-excited structures typically rely on accurate models of the system dynamics. Restoring forces in these structures can exhibit highly non-linear characteristics, thus accurate non-linear system identification is critical. Parametric system identification approaches are commonly used, but require a priori knowledge of restoring force characteristics. Non-parametric approaches do not require this a priori information, but they typically lack direct associations between the model and the system dynamics, providing limited utility for health monitoring and damage detection. In this paper a novel system identification approach, the Intelligent Parameter Varying (IPV) method, is used to identify constitutive non-linearities in structures subject to seismic excitations. IPV overcomes the limitations of traditional parametric and non-parametric approaches, while preserving the unique benefits of each. It uses embedded radial basis function networks to estimate the constitutive characteristics of inelastic and hysteretic restoring forces in a multi-degree-of-freedom structure. Simulation results are compared to those of a traditional parametric approach, the prediction error method. These results demonstrate the effectiveness of IPV in identifying highly nonlinear restoring forces, without a priori information, while preserving a direct association with the structural dynamics.

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Soheil Saadat, Gregory D. Buckner, Tadatoshi Furukawa, and Mohammad N. Noori "Nonlinear system identification of base-excited structures using an intelligent parameter varying (IPV) modeling approach", Proc. SPIE 5049, Smart Structures and Materials 2003: Modeling, Signal Processing, and Control, (1 August 2003); https://doi.org/10.1117/12.484066

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KEYWORDS

System identification

Artificial neural networks

Neodymium

Error analysis

Systems modeling

Damage detection

Complex systems

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