No abstract

This study consists in the evaluation of the use of an artificial neural network of modular architecture in building probabilistic constant life diagrams. Therefore, an algorithm developed in previous studies which was applied to achieve deterministic values has proved itself viable when at least three S-N curves were used. For this case, the probability S-N curves were used for training and validation of the modular network based on the generalized power law and a probability of 5% for failure has been considered. In addition, three composite materials were evaluated with a considerable number of tests to better assess the model.

The aim of this study was to compare the results obtained using the piecewise nonlinear model with those of a modular network (MN) architecture in modelling fatigue behaviour. We used a database consisting of ten materials derived from the literature and represented by the following acronyms: C10, C12, DD16, HTA‐913, IM7–977, MAT(0)2, T800–5245, T800H‐3631, QQ1 and QQ1T. The results obtained by MN were compared with those of the PNL. The results for both the PNL and MN model were satisfactory for the fatigue behaviour of glass fiber‐based composite materials, but for carbon fiber‐based composites only MN showed satisfactory results.