Neural network (NN) models have made a significant impact on
fatigue-related engineering communities and are expected to increase
rapidly soon due to the recent advancements in machine learning and
artificial intelligence. A comprehensive review of fatigue modeling
methods using NNs is lacking and will help to recognize past
achievements and suggest future research directions. Thus, this paper
presents a survey of 251 publications between 1990 and July 2021. The NN
modeling in fatigue is classified into five applications: fatigue life
prediction, fatigue crack, fatigue damage diagnosis, fatigue strength,
and fatigue load. A wide range of NN architectures are employed in the
literature and are summarized in this review. An overview of important
considerations and current limitations for the application of NNs in
fatigue is provided. Statistical analysis for the past and the current
trend is provided with representative examples. Existing gaps and future
research directions are also presented based on the reviewed articles.
Fatigue modeling using neural networks: A comprehensive review
