2023
DOI: 10.1186/s10033-023-00876-8
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Crack Growth Rate Model Derived from Domain Knowledge-Guided Symbolic Regression

Abstract: Machine learning (ML) has powerful nonlinear processing and multivariate learning capabilities, so it has been widely utilised in the fatigue field. However, most ML methods are inexplicable black-box models that are difficult to apply in engineering practice. Symbolic regression (SR) is an interpretable machine learning method for determining the optimal fitting equation for datasets. In this study, domain knowledge-guided SR was used to determine a new fatigue crack growth (FCG) rate model. Three terms of th… Show more

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Cited by 9 publications
(2 citation statements)
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“…The result of the analysis is then stored in Fracture Mechanics Results as structured text files and plots. The large amount of stored data, in the long-term, enables data-centric analyses, including techniques such as clustering, machine learning, and symbolic regression 60 . Such techniques need data to uncover patterns, make predictions, or build new physical models 46 .…”
Section: Methodsmentioning
confidence: 99%
“…The result of the analysis is then stored in Fracture Mechanics Results as structured text files and plots. The large amount of stored data, in the long-term, enables data-centric analyses, including techniques such as clustering, machine learning, and symbolic regression 60 . Such techniques need data to uncover patterns, make predictions, or build new physical models 46 .…”
Section: Methodsmentioning
confidence: 99%
“…The result of the analysis is then stored in Fracture Mechanics Results as structured text les and plots. The large amount of stored data, in the long-term, enables datacentric analyses, including techniques such as clustering, machine learning, and symbolic regression [57]. All these techniques need data to uncover patterns, make predictions, or build new physical models [43].…”
Section: Crackpymentioning
confidence: 99%