Fatigue modeling using neural networks: A comprehensive review

Arizona State University

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Abstract

Abstract Neural network (NN) models have significantly impacted fatigue‐related engineering communities and are expected to increase rapidly 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…

Citation impact

236
total citations
FWCI
27.31
Percentile
100%
References
257
Citations per year

Authors

2

Topics & keywords

Keywords
  • Artificial neural network
  • Fatigue testing
  • Computer science
  • Range (aeronautics)
  • Engineering
  • Artificial intelligence
  • Machine learning
  • Structural engineering
UN Sustainable Development Goals
  • Responsible consumption and production
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