Abstract
The optimal stochastic distribution of carbon nanotubes (CNTs) in nanoreinforced polymer composite of a cantilevered microbeam is investigated. Finite-element simulations of the CNT-reinforced microbeams were conducted to obtain data for training an artificial neural network to construct a surrogate model. This model was then used in an optimization routine to determine the optimal CNT distribution in the microbeam with inclusion of an uncertainty in the dispersion of CNTs across the microbeam. The results obtained within the framework of this model showed an improvement compared with those reported in the literature.