###### Abstract  
The implementation of a machine learning approach to predict vibration suppression, as derived from nanocomposite laminates with piezoelectric shunted systems, is studied in this article. Datasets providing the vibration response and vibration attenuation are developed using parametric finite element simulations. A graphene/fibre-reinforced laminate cantilever beam is used in those simulations. Parameters, including the graphene and fibre reinforcements content, as well as the fibre angles, are among the inputs. Output is the vibration suppression achieved by the piezoelectric shunted system. Artificial Neural Networks are trained and tested using the derived datasets. The proposed methodology can be used for a fast and accurate prediction of the vibration response of nanocomposite laminates. artificial neural network; graphene nanoplatelets (GPLs); laminated nanocomposites; piezoelectric shunt circuit; single-mode damping; free vibration; machine learning; neural network metamodels +
Footnote †: journal: Applied Mechanics and Mechanics Mechanics  
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Footnote 1: [[https://doi.org/10.3390/sca20017](https://doi.org/10.3390/sca20017)]([https://doi.org/10.3390/sca20017](https://doi.org/10.3390/sca20017))  
Footnote †: journal: Applied Mechanics and Mechanics  
through either the direct effect (i.e., production of an electrical charge under pressure) or the inverse effect (i.e., mechanical strain induced when subjected to an electric field) [9]. Many of those studies focus on the passive vibration control of structures by using shunted piezoelectric elements connected to electric circuits [10; 11; 12]. In recent times, there has been increasing attention within the scientific community on the incorporation of piezoelectric layers into graphene nanoplatelets composite structures in order to actively control and reduce unexpected mechanical responses. Several studies have been conducted to investigate the mechanical behaviour of such structures. In [13], the dynamic response and the active vibration control of functionally graded (FG) multilayer GPLs reinforced composite plates integrated with piezoelectric layer were studied. [PERSON] et al. [14] studied the active vibration control and vibration characteristics of a sandwich-thin cylindrical shell whose intermediate layer is made of graphene-reinforced composite that is bonded with integrated piezoelectric actuator and sensor layers at its outer and inner surfaces. Also, parameters like weight fractions, distribution patterns and geometrical sizes of graphene platelets, temperature variations, thicknesses of layers, the feedback control gain on the vibration characteristics, and the active vibration control of the novel sandwich cylindrical shell are discussed. The static and dynamic response of smart FG microplates reinforced by GPLs and integrated with piezoelectric layers under concurrently mechanical and electrical loads was studied in [15]. Furthermore, active control of responses for an intelligent FG microplate, including the structural damping effect under simultaneous mechanical and electrical loads, was examined. The active control of vibrations of a nanocomposite microplate reinforced by graphene platelets as host layer bonded with two piezoelectric layers as sensor and actuator layers in the thermal environment was studied in [16]. A closed-loop PD controller based on the velocity and the displacement feedback signals is developed for the active control of vibrations of the piezo-electric functionally graded (FG) GPL microplate. The effects of the control gain values, the material length scale parameter, the boundary condition type, the GPL distribution pattern, and the GPL weight fraction on the dynamic response of the piezoelectric GPL microplate are investigated. [PERSON] et al. [17] studied the nonlinear vibration suppression of a piezoelectric functionally graded graphene-reinforced laminated composite cantilever using a positive position feedback control strategy.