Natural frequency prediction of functionally graded graphene-reinforced nanocomposite plates using ensemble learning and support vector machine models

Functionally graded materials (FGMs) are modern engineering materials with increasing application in various industrial fields. In this study, the free vibration behavior of graphene-reinforced FGM plate is investigated using finite element method and machine learning (ML) approaches. For this purpose, three advanced ML models including ensemble learning algorithms (bootstrap aggregation and gradient boosting) and Gaussian support vector machine are employed to predict the natural frequency of functionally graded graphene/epoxy nanocomposite plates. In this regard, first, hyperparameter optimization is carried out using Bayesian optimization algorithm. Then, regression analysis is performed using the aforementioned ML approaches. According to the obtained results, all ML models have a high coefficient of determination (more than 96%) with low mean squared error (MSE) values. However, the best performance is related to the gradient boosting method, followed by support vector machine and bootstrap aggregation, respectively. Finally, the significance degree of involved parameters on natural frequency is estimated using the Shapley values concept. The obtained results reveal that the most significant parameters affecting the natural frequency of graphene-reinforced FGM plates are clamp type, the volume fraction of graphene, followed by thickness ratio and distribution pattern, respectively.