Abstract
The aim of this article was to develop an artificial neural network (ANN) architecture capable of estimating the service life of a family of aluminum conductor cables, using the K constant from the Poffenberger-Swart formula as reference, and specific weight (W). The database used to train and test the neural architectures was obtained from fatigue tests conducted at the cable laboratory of the Fatigue, Fracture and Materials Group (GFFM) of the Federal University of Brasilia (UnB). The ANNs were used to construct constant life curves for these conductor cables and the results compared with the values obtained for other ANN models presented in earlier studies. In addition to reducing the complexity of the ANN architecture, the results show that incorporating the Poffenberger-Swart formula to the ANN model also decreases the error obtained between the ANN and the values used in training and testing. The functions code produced in this paper will be provided in open-source format (https://github.com/raimundo-freire-junior/AL-CONDUCT-CABLES).
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