Development of system identification algorithm for health monitoring of infrastructures

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System identification technique to autoassociate hysteretic characteristics is proposed by learning a nonlinear dynamic structure which is represented by a system dynamics model. As this regard, neural network's learning serves as the means. The criterion function is defined by the sum of squared output errors which takes into account the energy of earthquake motion input to the structure. In order to obtain a regularly directed learning, effective use of the structural learning is considered as an alternative method for parameter and system identification problems. A global and local iteration procedure is proposed to obtain stable and fast convergency in the learning of recurrent networks.A method is developed to identify parameters on a hysteretic restoring system of non-degrading type by applying the wavelet transform. By this method, a nonlinear model of non-degrading type equivalent to any hysteretic system may be identified in terms of the model's parameters at the stage of their stable convergency to optimal ones. Numerical examples give satisfactory results to identify dynamic parameters of model structure with 3-degree-of-freedom.Effective identification scheme for hysteretic, degrading multi-degree of freedom shear beam structures is developed using importance sampling and rejection sampling filters. The effects and the accuracy of this procedure are analyzed by comparing with the conventional methods. Using converged parameters resimulated responses and hysteretic force characteristic are compared with known true ones. Stable solutions as well as their fast convergency to the optimum ones are obtained. It is found by numerical examples that the proposed method is a powerful tool for parameter identification.