Efficient optimal seismic design method of passive energy dissipation systems based on the inelasticity-separated finite element method

Installing passive energy dissipation dampers in the main structure is an effective way to reduce structural seismic response. The traditional optimal design of passive energy dissipation systems requires a massive nonlinear time history analysis, which is an extremely inefficient process and, as a result, limits its application in practice. In this paper, an efficient optimal design method for dampers is proposed based on the inelasticity-separated finite element method (IS-FEM) and genetic algorithm. The IS-FEM is a novel efficient structural nonlinear analysis method that can avoid the real-time updating and decomposition of the structural global stiffness matrix in a traditional structure analysis and only requires a small-scale Schur complementary matrix representing local nonlinear behavior being decomposed per iteration. To make the IS-FEM available for the analysis of structures equipped with an energy dissipation system, the inelasticity-separated governing equations of displacement-based dampers are derived first. Then, an improved constrained optimal genetic algorithm based on population feasibility is presented to enhance the searching ability near the feasible domain boundary. Finally, an efficient damper optimization design method is proposed by combining the developed genetic algorithm with the proposed IS-FEM-based analysis procedure for passive energy dissipation structures. The accuracy and efficiency of the proposed method are verified by designing a set of metallic yield dampers to retrofit a steel structure. In this paper, a novel optimal design method for dampers was proposed based on inelasticity-separated finite element method (IS-FEM) and genetic algorithm. During the process of optimization, the proposed method enhances the searching ability near the feasible domain boundary and avoids the recalculation of structural tangent stiffness for seismic response analysis, so that the computational complexity of the algorithm can be reduced significantly. image