Estimating fatigue behavior of a family of aluminum overhead conductors using ANNs

This study aimed to create an artificial neural network (ANN) architecture capable of estimating the fatigue behavior of aluminum overhead conductors, considering specific weight (W) and bending stiffness (EI) as parameters of influence. ANN training and testing is conducted by using a dataset obtained from fatigue tests carried out in a 50 m resonant bench at the University of Brasilia (UnB). ANNs are used to construct constant life diagrams for this family of conductors, and to compare the results obtained experimentally. Our findings show that for the architectures analyzed, it is possible to accurately estimate the fatigue behavior of this family of aluminum conductors.