Tadatoshi Furukawa

An intelligent parameter varying (IPV) approach for non-linear system identification of base excited structures

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 non-linear restoring forces, without a priori information, while preserving a direct association with the structural dynamics.

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

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.

Identification of seismic damage to structural buildings using the quasi-Newton method

In order to restore damaged buildings affected by earthquake excitations, it is important to identify damaged elements in terms of their dynamic properties; stiffness, mass and damping coefficients. In this paper, a simple method is proposed for identification of the damaged part. By considering all unknown dynamic properties of structure as variables of a target function f in nonlinear programming problem, the damage identification problem can be replaced by a typical unconstrained minimization problem. The target function is defined as f equals (Sigma) [ (yn,i) - (yn*) ]2 where (yn,i) is structural response at the time of t equals (Delta) X n derived from i-th trial variable (Xi), and (yn*) means observed response, respectively. In order to achieve quick convergence, quasi- Newton method and BFGS formulae are adopted for minimizing the target function. Two decision problems are discussed. One is the choice of structural response; displacement, velocity or acceleration. The second is the kind of external excitations that should be adopted. By observing three dimensional graphics. It appeared that good convergence can be achieved by adopting displacement response and sinusoidal excitation. Furthermore, it appeared that we should not evaluate the identified properties only from the response diagrams.