PREDICTION OF FATIGUE CRACK GROWTH DIAGRAMS BY METHODS OF MACHINE LEARNING UNDER CONSTANT AMPLITUDE LOADING
Keyword(s):
Important structural elements are often under the action of constant amplitude loading. Increasing their lifetime is an actual task and of great economic importance. To evaluate the lifetime of structural elements, it is necessary to be able to predict the fatigue crack growth rate (FCG). This task can be effectively solved by methods of machine learning, in particular by neural networks, boosted trees, support-vector machines, and k -nearest neighbors. The aim of the present work was to build the fatigue crack growth diagrams of steel 0.45% C subjected to constant amplitude loading at stress ratios R = 0, and R = –1 by the methods of machine learning. The obtained results are in good agreement with the experimental data.
2007 ◽
Vol 348-349
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pp. 333-336
2012 ◽
Vol 525-526
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pp. 221-224
2021 ◽
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2018 ◽
Vol 114
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pp. 189-197
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2021 ◽
Vol 1016
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pp. 1193-1198
2003 ◽
Vol 27
(6)
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pp. 923-929
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2020 ◽
Vol 229
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pp. 106933
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