Study of the fatigue behavior of composites using modular ANN with the incorporation of a posteriori failure probability

Abstract

To study the fatigue behavior of composites, a large number of experimental assays for both deterministic and probabilistic analysis are needed. Recent studies have demonstrated mathematical and methodological models that are applied to determine the deterministic fatigue of composite materials, but to date, no reliable model has been developed to analyze probabilistic fatigue behavior using a small amount of experimental data and considering failure probability in analysis. As such, this study aimed to develop an artificial neural network (ANN) with modular architecture in order to model probabilistic fatigue behavior, using only three S-N curves in training and applying a posteriori failure probability (after ANN training). To that end, two methodologies were used to obtain Weibull distribution parameters and this result was incorporated into deterministic ANN architecture The advantage of this strategy is that training and using a deterministic ANN has demonstrated repeatability (robustness) in the training and generalization of the final result, while attempts to train an ANN with probabilistic data do not always obtain satisfactory results after training.