A Novel Strain Stiffening Model for Magnetorheological Elastomer Base Isolator and Parameter Estimation Using Improved Particle Swarm Optimization

International Center for Numerical Methods in Engineering (CIMNE)
Publication Type:
Conference Proceeding
Proceedings of the 6th edition of the World Conference of the International Association for Structural Control and Monitoring (IACSM), 2014
Issue Date:
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In order to fully utilize the advantages of magnetorheological elastomer (MRE) base isolator for seismic protection of civil structures, a high fidelity model should be established to characterize its nonlinear hysteresis for its implementation in structural control. In this paper, a novel strain stiffening model is developed to capture this unique characteristic. In this model, a strain stiffening component, which described the unique viscos-elastic behavior of the device, is incorporated with a Voigt element, which portrays the solid-material behavior. The new model, as an attractive feature, maintains a relationship between the isolator parameters and physical force-displacement nonlinear phenomenon and decreases the complexity in other existing models. In addition to the proposed model, an improved optimization algorithm based on particle swarm optimization (IPSO) is designed to identify the model parameters by utilizing experimental force-displacement-velocity data acquired from various loading conditions. In this new algorithm, the mutation operation in genetic algorithm is utilized for helping the model solution avoiding the local optimum. The superiority of the proposed model and parameter solving algorithm is validated by comparing them with the classical Bouc-Wen model and other optimization algorithms through the error analysis, respectively. The comparison results show that the proposed model can exactly predict the force-displacement and force-velocity responses at both small and large displacements, and has a smaller root-mean-square (MSE) error than the Bouc-Wen model. Compared with other optimization algorithm, the IPSO not only has a faster convergence rate, but also obtains the satisfactory parameters identification results.
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