Speaker
Description
Active vibration control through Active Mass Dampers (AMDs) represents an effective and increasingly widespread strategy for reducing the dynamic response of civil structures subjected to actions such as wind, earthquakes, and other induced dynamic loads, both in the design phase of new buildings and in the retrofit of existing structures.
However, the performance of such systems does not depend solely on the adopted control law; it is also strongly influenced by the placement of the actuators within the structure. In this context, the present study addresses the problem of optimizing the placement of AMDs through the use of Genetic Algorithms (GAs), with the aim of maximizing vibration mitigation effectiveness.
The developed methodology integrates a non-classical numerical structural model (capable of accounting for the contribution of concentrated damping) with an evolutionary optimization procedure. The analysis is carried out using numerical models of the structure coupled with the AMDs and time-domain simulations based on modal superposition, in order to assess the influence of different placement configurations on the overall performance of the controlled system. Different formulations of the optimization problem are considered and compared, based on modal criteria, dynamic response indicators, and multi-objective functions, analyzing the impact of the various parameters to be maximized or minimized.
The results obtained from representative case studies show that evolutionary optimization makes it possible to identify non-intuitive yet highly effective placements, achieving significant reductions in floor accelerations and improvements in structural comfort and safety.
This work also highlights the flexibility of the method in retrofit interventions, where architectural and structural constraints impose limited design choices. The integration of dynamic modeling and evolutionary optimization thus proves to be an effective tool for supporting designers in defining active control strategies, contributing to the development of intelligent and adaptive solutions for dynamic risk mitigation in existing constructions.