###### Abstract
To the authors' best knowledge, even though numerous studies have been conducted about the active control of multilayered GPL reinforced composite structures, nonetheless, there is no paper relating the passive vibration control of such structures by utilising shunted piezoelectric elements. On the other hand, in recent years, machine learning (ML), a sub-category of artificial intelligence, has provided researchers and engineers with novel methods for engaging with the data collected. In particular, ML techniques have shown great potential in material design. Transfer learning-based deep neural networks were developed by [PERSON] [18] to predict the mechanical properties of various graphene-reinforced nanocomposites, even with limited data samples. A machine-learning model is utilised to estimate the temperature-dependent moduli of neat, thermally reduced graphene and covalently functionalised graphene/epoxy nanocomposites [19]. The governed mathematical expressions have been used to solve the buckling problem of beams fabricated from such nanocomposites in the presence of a thermal gradient. Numerous authors have chosen to utilise ML techniques in addressing the vibration problems related to composite structures. [PERSON] et al. [20] introduced an appropriate approach for resolving partial differential equations through the employment of artificial neural networks and an adaptive collocation technique. [PERSON] et al. [21] proposed a novel deep collocation method for addressing Kirchhoff plate bending by first creating randomly distributed collocation points. Very few papers have been reported on the vibration analysis of graphene-reinforced nanocomposite structures using a machine learning-based approach. An ML-assisted micromechanics model for the investigation of the buckling response of functionally graded hydrogen-functionalised graphene-reinforced beams at different temperatures has been developed in [22]. A machine learning based probabilistic model is proposed in [23] to perform the vibration analysis of functionally graded porous plates reinforced with graphene nanoplatelets with statistical variation in the elastic moduli of constituents, porosity index, and weight fraction of graphene nanoplatelets. Based on the preceding literature, we found that the problem of vibrations of graphene/fibre reinforced (GPLFR) nanocomposite beams with shunted piezoelectric patches using machine learning based techniques has not been explored to the best of the authors' knowledge. Therefore, the objective of the present study is to propose an efficient Artificial Neural Network (ANN) model in order to investigate the variation in the natural frequency as well as the vibration-damping performance of a GPLFR nanocomposite beam considering variation in material properties and electric circuit components. Emphasis is given to predicting the vibration suppression of the system. A key aspect of the investigation is to evaluate the level of accuracy that can be achieved. As will be shown, the accuracy of the trained ANN to predict the vibration response is quite high, indicating that the proposed approach can safely be adopted. Another goal of the study is to highlight the benefits of adopting machine learning in this field, to decrease the computational cost and effort. It is noted that within the traditional path, this problem would normally involve developing a finite element model, probably in a programming code environment, since not all commercial finite element packages can offer physics-oriented tools, like the shunted piezoelectric elements. Instead, calling a trained ANN to predict the response can be used to drastically reduce the effort to develop and solve the equivalent model.