Seismic response analysis of damper-connected adjacent structures with stochastic parameters

Abstract

Dynamic response analysis of damper connected adjacent multi-story structures with uncertain parameters is carried out. A formula of the multi degree of freedom (MDOF) for the structure-damper system with stochastic parameters is derived. The uncertainties of mass and stiffness are taken into consideration firstly. The ground acceleration is represented by Kanai-Tajimi filtered non-stationary process. The mean square random responses of structural displacement and story drift are chosen as the optimization objective. The variations of mean square responses of top floor displacements and bottom story drifts in neighboring structures with the damper stiffness and damping coefficient are analyzed in detail. Through the parametric study, the acquiring optimum parameters of damper are regarded as numerical results. Then, a reducing order model of the MDOF system for adjacent structures with mean parameters is presented. The explicit expressions for determining optimal parameters of Kelvin model-defined damper which is used to connect adjacent single degree of freedom (SDOF) structures subjected to a white-noise excitation are employed to achieve the appropriate damper parameters, which are called theory results. Through a comparative study, it can be found that the theory values of damper parameters are consistent with the results based on extensive parametric studies. The analytical results can be obtained by using the first natural frequencies and the total mass of the adjacent deterministic structures with mean parameters. The analytical formulas can be used to find appropriate parameters of damper between adjacent structures for engineering applications. The performance of damper is investigated on the basis of mitigations of mean square random responses of inter-story drifts, displacements and accelerations in adjacent structures. The numerical results demonstrate the robustness of coupled building control strategies.