2023
DOI: 10.3390/s23031074
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Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data

Abstract: Machine learning has the potential to enhance damage detection and prediction in materials science. Machine learning also has the ability to produce highly reliable and accurate representations, which can improve the detection and prediction of damage compared to the traditional knowledge-based approaches. These approaches can be used for a wide range of applications, including material design; predicting material properties; identifying hidden relationships; and classifying microstructures, defects, and damag… Show more

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Cited by 8 publications
(15 citation statements)
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References 64 publications
(96 reference statements)
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“…This broader evaluation provides insights into the strengths and weaknesses of the machine learning models, facilitating the development of more robust and accurate prediction methods applicable to a range of materials. It is important to acknowledge the differences between these materials and interpret the results accordingly, considering the unique characteristics of each material type [1,2].…”
Section: Specimen Parameters and Experimental Data Collectionmentioning
confidence: 99%
See 4 more Smart Citations
“…This broader evaluation provides insights into the strengths and weaknesses of the machine learning models, facilitating the development of more robust and accurate prediction methods applicable to a range of materials. It is important to acknowledge the differences between these materials and interpret the results accordingly, considering the unique characteristics of each material type [1,2].…”
Section: Specimen Parameters and Experimental Data Collectionmentioning
confidence: 99%
“…and accurate prediction methods applicable to a range of materials. It is important to acknowledge the differences between these materials and interpret the results accordingly, considering the unique characteristics of each material type [1,2].…”
Section: Specimen Parameters and Experimental Data Collectionmentioning
confidence: 99%
See 3 more Smart Citations