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Effectiveness evaluation and optimal design of nonlinear viscous dampers for inelastic structures under pulse‐type ground motions
Author(s) -
Xie Yazhou,
Zhang Jian,
Xi Wang
Publication year - 2018
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/eqe.3109
Subject(s) - nonlinear system , structural engineering , damper , displacement (psychology) , acceleration , engineering , control theory (sociology) , dimensionless quantity , computer science , physics , mechanics , classical mechanics , control (management) , psychology , quantum mechanics , artificial intelligence , psychotherapist
Summary This paper addresses the effectiveness and optimal design of nonlinear viscous dampers for inelastic structures. First, a nonlinear damping index is derived by using the dimensional analysis to estimate the damping induced by supplemental nonlinear dampers on inelastic single degree of freedom (SDOF) structures. Subsequently, the effects of the added nonlinear damping on the seismic responses of inelastic SDOF systems are analyzed in dimensionless forms when subject to various near‐fault ground motions. The structure‐to‐motion frequency ratio, the motion characteristics, and the structural nonlinearity are the main factors that will affect the damping effectiveness. Especially, it is shown that adding nonlinear viscous dampers will decrease displacement demands yet sometimes lead to amplified acceleration responses. Furthermore, an equivalency procedure is developed to match the inelastic multi‐degree of freedom (MDOF) structure that is equipped with multiple nonlinear viscous dampers to its corresponding SDOF system. Such equivalency justifies that the analysis results for the viscous damping efficiency on SDOF systems can be congruously applied to realistic multi‐story structures. Finally, the optimal designs of nonlinear dampers for MDOF inelastic structures are identified by implementing a hybrid genetic optimization framework along with a robust performance index.