Fatigue modeling using neural networks: A comprehensive review
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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
2Topics & keywords
Topics
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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