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Teaching-learning based optimization for parameter estimation of double tuned mass dampers

Sinan Melih Niğdeli, Gebrail Bekdaş



The classical methods for parameter estimation of tuned mass dampers are well known simple formulations, but these formulations are only suitable for multiple degree of freedom structures by considering a single mode. If special range limitation of tuned mass dampers and inherent damping of the main structure are considered, the best way to estimate the parameters is to use a numerical method. The numerical method must have a good convergence and computation time. In that case, metaheuristic methods are effective on the problem. Generally, metaheuristic method is inspired from a process of life and it is formulated for several steps in order to reach an optimal goal. Differently from the single tuned mass dampers, double tuned mass dampers can be also used for the reduction of vibrations. In civil structures, earthquake excitation is a major source of vibrations. In this study, optimum double tuned mass dampers are investigated for seismic structures by using a wide range of earthquake records for global optimum. As an optimization algorithm, teaching learning based optimization is employed. In this algorithm, the teaching and learning phases of a class are modified for optimization problems. The optimization of double tuned mass damper is more challenging than the single ones since the number of design variable is doubled and the design constraint about the stroke of the both masses must be considered. The proposed method is compared with the existing approaches and the methodology is feasible for parameter estimation of double tuned mass dampers.


optimization; teaching-learning based optimization; double tuned mass dampers; stroke capacity; earthquakes

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Peer-review under responsibility of the organizing committee of ACE2016.


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