PARAMETER UNCERTAINTY ANALYSIS OF MNS MODEL FOR MAGNETO-RHEOLOGICAL DAMPER

A Magneto-Rheological (MR) damper provides a viable alternative for the vibration control of structures under earthquakes and winds. The realistic modeling of MR dampers is essential for optimization of semi-active control laws and structural response prediction. The deterministic model parameters obtained from traditional optimization methods may not provide an accurate prediction of damper output due to uncertainties inherent to phenomenological models. Using Markov Chain Monte Carlo (MCMC) method, this study presents a probabilistic study for Maxwell Nonlinear Slider (MNS) model. By comparing with existing experimental results of a large-scale 200 kN MR damper, it is demonstrated that the probabilistic model can better predict the force output and energy dissipation of a MR damper under both predefined sinusoidal displacements and damper deformation from real-time hybrid simulations. This study therefore provides a probabilistic alternate for better response prediction of structures with the presence of uncertainties inherent to the phenomenological model of MR dampers.