Component-wise damage detection by neural networks and refined FEs training

Pagani, Alfonso; Enea, M.; Carrera, Erasmo

doi:10.1016/j.jsv.2021.116255

Cited by 14 publications

(1 citation statement)

References 25 publications

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“…An analytical model is necessary to solve an inverse problem, which necessitates considerable effort in developing a mathematical framework that is both accurate and reliable. Artificial Neural Network (ANN) approaches can be applied to damage assessment techniques to overcome the complexity of framing mathematical frameworks [9,10]. ANN is used to anticipate damage features since neural networks are now being used as universal function approximators for difficult problems.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network based delamination prediction in composite plates using vibration signals

Sreekanth

Senthilkumar

Reddy

2022

Frattura Ed Integrità Strutturale

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Dynamic loading on composite components may induce damages such as cracks, delaminations, etc. and development of an early damage detection technique for delaminations is one of the most important aspects in ensuring the integrity and safety of composite components. The presence of damages such as delaminations on the composites reduces its stiffness and further changes the dynamic behaviour of the structures. As the loss in stiffness leads to changes in the natural frequencies, mode shapes, and other aspects of the structure, vibration analysis may be the ideal technique to employ in this case. In this research work, the supervised feed-forward multilayer back-propagation Artificial Neural Network (ANN) is used to determine the position and area of delaminations in GFRP plates using changes in natural frequencies as inputs. The natural frequencies were obtained by finite element analysis and results are validated by experimentation. The findings show that the suggested technique can satisfactorily estimate the location and extent of delaminations in composite plates.

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Section: Introductionmentioning

confidence: 99%