Comparative study between ANN models and conventional equations in the analysis of fatigue failure of GFRP

Abstract The purpose of this paper is to assess the applicability of two artificial neural networks (ANN) architecture, perceptron ANN, modular ANN, and Adam’s equation in the modeling of fatigue failure in polymer composites, more specifically in glass fiber reinforced plastic (GFRP). In the application of the model using ANN we show the feasibility of obtaining good results with a small number of S–N curves. The other model used involves applying empirical equations known as Adam’s equations. A comparative study on the application of the aforementioned models is developed based on statistical tools such as correlation coefficient and mean square error. For this analysis we used composite materials in the form of laminar structures with distinct stacking sequences, which are applied industrially in the construction of large reservoirs. Reinforcements consist of mats and bidirectional textile fabric made of E-glass fibers soaked in unsaturated orthophthalic polyester resin. These were tested for six different stress ratios: R = 1.43, 10, −1.57, −1, 0.1, and 0.7. The results showed that although ANN modeling is in the initial phase, it has great application potential.