Nonparametric dynamic modeling of a non-linear frame structure with MR dampers

Abstract

Identification of nonlinear dynamic systems using vibration measurements is crucial for efficient and reliable damage detection in structural health monitoring and control system design. Because of the complexity of control devices, it is usually difficult to model the nonlinear control devices with enough accuracy in a parametric form. In this study, a multi-storey steel-frame model structure equipped with magneto-rheological (MR) dampers, which were employed to introduce nonlinear phenomena to the model structure, was modeled with a neural network in a nonparametric way. Corresponding to the availability of dynamic response measurements, two different network models were proposed to predict the vibration response of the nonlinear model structure. Raw dynamic response measurements of the model structure under a certain impulse excitation was employed to train the two neural network models and the generality of the trained neural network models were validated in the form of forecasting the raw test dynamic response measurements of the model structure under other impulse excitation conditions. Results show that two neural network models provide a reliable way for the modeling of nonlinear dynamic structures and present a useful way for the control system design of engineering structures equipped with nonlinear control devices.