This work deals with the multi-objective optimization of single Tuned Mass Dampers, focusing on buildings subject to low-moderate earthquakes. The novelty consists in considering both economic and performance criteria. The economic objective is represented by the cost of the device, directly related to its mechanical parameters, while the ratio between the absolute acceleration of the protected structure and the unprotected one is assumed as performance parameter. This latter parameter allows in fact to evaluate the damage level and the behavior of both components and equipment. A multi-objective optimization problem is then so formulated and the Non-dominated Sorting Genetic Algorithm, in its second version, is applied to obtain the Pareto optimum solutions. Finally, by a sensitivity analysis, the optimum solutions are commented with respect to some input data.
Optimal design of tuned mass dampers by performance-cost analysis / Greco, Rita; Marano, Giuseppe C.; Fiore, Alessandra. - 1:(2017), pp. 2161-2170. (Intervento presentato al convegno 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, COMPDYN 2017 tenutosi a Rhodes Island, Greece nel June 15-17, 2017) [10.7712/120117.5557.18329].
Optimal design of tuned mass dampers by performance-cost analysis
Greco, Rita;Marano, Giuseppe C.;Fiore, Alessandra
2017-01-01
Abstract
This work deals with the multi-objective optimization of single Tuned Mass Dampers, focusing on buildings subject to low-moderate earthquakes. The novelty consists in considering both economic and performance criteria. The economic objective is represented by the cost of the device, directly related to its mechanical parameters, while the ratio between the absolute acceleration of the protected structure and the unprotected one is assumed as performance parameter. This latter parameter allows in fact to evaluate the damage level and the behavior of both components and equipment. A multi-objective optimization problem is then so formulated and the Non-dominated Sorting Genetic Algorithm, in its second version, is applied to obtain the Pareto optimum solutions. Finally, by a sensitivity analysis, the optimum solutions are commented with respect to some input data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.