Durability prediction of glass/epoxy composite using artificial neural network

This research article examines the prediction capability of the artificial neural network for the durability of FRP composite. In this study the glass/epoxy composite was immersed under harsh environment for the duration of 11 years. The temperature of the seawater was maintained at 23°C, 45°C, and 65°C. The durability of the samples was evaluated in terms of the tensile strength of the conditioned samples. Furthermore, the feedforward backpropagation technique was used in which exposure temperature (°C) and time (months) was used as an input variable and tensile strength was set as an output variable. The results revealed that the established prediction model is promising for the forecasting of the durability of composite.