Structural control has been comprehensively studied over the world as a multidisciplinary research field. The present work is motivated by an attempt to give a common frame to the recent research and applications of structural control technology in civil engineering across Europe. They include novel passive dampers, functional materials and semi-active dampers, active control systems, and their performance investigations. Design methods for the vibrations reduction of buildings, bridges, and wind turbines are discussed with reference to case studies. Control algorithms and dimension reduction techniques are also studied. Adaptation strategies and techniques based on the potential offered by piezoelectricity are reviewed

A parametric beam lattice model is formulated to analyse the propagation properties of

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Recently, an increasing research effort has been dedicated to analyse the transmission and dispersion properties of periodic acoustic metamaterials, characterized by the presence of local resonators. Within this context, particular attention has been paid to the optimization of the amplitudes and center frequencies of selected stop and pass bands inside the Floquet-Bloch spectra of the acoustic metamaterials featured by a chiral or antichiral microstructure. Novel functional applications of such research are expected in the optimal parametric design of smart tunable mechanical filters and directional waveguides. The present paper deals with the maximization of the amplitude of low-frequency band gaps, by proposing suitable numerical techniques to solve the associated optimization problems. Specifically, the feasibility and effectiveness of Radial Basis Function networks and Quasi-Monte Carlo methods for the interpolation of the objective functions of such optimization problems are discussed, and their numerical application to a specific acoustic metamaterial with tetrachiral microstructure is presented. The discussion is motivated theoretically by the high computational effort often needed for an exact evaluation of the objective functions arising in band gap optimization problems, when iterative algorithms are used for their approximate solution. By replacing such functions with suitable surrogate objective functions constructed applying machine-learning techniques, well-performing suboptimal solutions can be obtained with a smaller computational effort. Numerical results demonstrate the effective potential of the proposed approach. Current directions of research involving the use of additional machine-learning techniques are also presented.