Neural networks for fatigue crack propagation predictions in real-time under uncertainty

Giannella, V.; Bardozzo, F.; Postiglione, A.; Tagliaferri, R.; Sepe, R.; Armentani, E.

doi:10.1016/j.compstruc.2023.107157

Computers & Structures

2023

DOI: 10.1016/j.compstruc.2023.107157

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Neural networks for fatigue crack propagation predictions in real-time under uncertainty

V. Giannella,

F. Bardozzo,

A. Postiglione

et al.

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2024

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Cited by 3 publications

References 63 publications

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Development of a Machine Learning Algorithm for Efficient Localization of Damage in a Composite Structure Using Random Forest Technique

Shinagam,

Maruvada,

Janjeti

et al. 2024

Iran J Sci Technol Trans Civ Eng

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Development of a Machine Learning Algorithm for Efficient Localization of Damage in a Composite Structure Using Random Forest Technique

Shinagam,

Maruvada,

Janjeti

et al. 2024

Iran J Sci Technol Trans Civ Eng

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Prediction of Helicopter Rotor Loads and Fatigue Damage Evaluation with Neural Networks

Graziani,

Prederi,

Trezzini

et al. 2024

Aerotec. Missili Spaz.

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In recent years, machine learning algorithms have experienced rapid advancement, driven by the exponential growth of data availability and computational capabilities. Among these algorithms, artificial neural networks stand out as one of the most renowned and effective classes, possessing the ability to discern relationships within data. In this study, we harness neural networks to deduce the relationship between flight mechanics parameters and resulting loads in an articulated rotor configuration. The accuracy of these algorithms hinges closely on the quality of the dataset used for training. Given that rotor loads manifest as time-periodic signals with precise harmonic content, we train dedicated neural networks to predict each harmonic individually. Subsequently, the load time history is reconstructed post hoc by amalgamating predictions from each individual network. Various network architectures are explored, and a sensitivity analysis is conducted on hyper-parameters to determine the optimal configuration for this specific application. Moreover, these predictions serve as input for a fatigue damage calculation algorithm.

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Study of fatigue crack propagation on modified CT specimens under variable amplitude loadings using machine learning

Santos,

Infante,

Barros

et al. 2024

International Journal of Fatigue

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Neural networks for fatigue crack propagation predictions in real-time under uncertainty

Cited by 3 publications

References 63 publications

Development of a Machine Learning Algorithm for Efficient Localization of Damage in a Composite Structure Using Random Forest Technique

Development of a Machine Learning Algorithm for Efficient Localization of Damage in a Composite Structure Using Random Forest Technique

Prediction of Helicopter Rotor Loads and Fatigue Damage Evaluation with Neural Networks

Study of fatigue crack propagation on modified CT specimens under variable amplitude loadings using machine learning

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