
The tuned mass damper is one of the most frequently employed structural control devices for mitigating dynamic vibrations in structures subjected to earthquake ground motions. Conventional tuned mass dampers require substantial mass to effectively reduce the structure’s vibration. However, implementing multiple-tuned mass dampers can also improve seismic performance while reducing the required mass. The dynamic characteristics of these devices play a critical role in enhancing the effectiveness of multiple-tuned mass dampers and the seismic performance of the structure. This study investigates the efficiency of double-tuned mass dampers and the optimization of their dynamic characteristics to minimize structural displacement and acceleration. The tuning process is carried out using a combination of Pareto front derived from seven multi-objective metaheuristic optimization algorithms with two objectives. The proposed methodology is applied to a 10-floor case study, using ground acceleration time histories to evaluate its seismic performance. To demonstrate the efficiency of the proposed method, the results are compared with those from a double-tuned mass damper system and an uncontrolled structure. The evaluation is carried out using seven earthquake ground motion records in addition to one benchmark record. The findings show that employing optimally tuned double-tuned mass dampers reduced acceleration by 30% and displacement by 50%. The numerical results confirmed that the proposed methodology effectively identifies the optimal double-tuned mass damper configuration under earthquake excitation.
seismic control, seismic response, evolutionary algorithm, multi-objective optimization, HT165.5-169.9, double tuned mass damper, global Pareto front, TA1-2040, Engineering (General). Civil engineering (General), City planning
seismic control, seismic response, evolutionary algorithm, multi-objective optimization, HT165.5-169.9, double tuned mass damper, global Pareto front, TA1-2040, Engineering (General). Civil engineering (General), City planning
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