ABSTRACT
A particle damper is composed of a volume of granular particles enclosed within a hollow cavity. Particles may be metallic, non-metallic or a mixture of various materials. The performance of particle damper is strongly nonlinear whose energy dissipation is derived from a combination of mechanisms including plastic collisions and friction between the particles and the walls and between the particles themselves. An optimized Support Vector Regression (SVR) model is built to predict the damping ratio of cantilever beam with particle damper, in which Genetic Algorithm (GA) is used to optimize the parameters of SVR model. The experiment proved that GA can select better SVR parameters, and set up “damping ratio—influencing parameters” prediction model more accurately, actual value and predicted value which obtained by the model has high consistency, average relative error is 10.3%.
