Comparative Study of Advance Smart Strain Approximation Method Using Levenberg-Marquardt and Bayesian Regularization Backpropagation Algorithm

This study aimed to develop a smart model prediction of strain calculation using fiber optic sensors and neural network. Optical parameters are obtained experimentally on a cantilever beam structure, under static loading conditions. Five variations are used by creating external damage to study strai...

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Veröffentlicht in:Materials today : proceedings Jg. 21; S. 1380 - 1395
Hauptverfasser: Wali, Ashwarya Sheel, Tyagi, Amit
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 2020
Schlagworte:
Finite element analysis (FEA)
Neural network
Optical parameters
Strain
Neural network
Optical parameters
Finite element analysis (FEA)
Strain
ISSN:2214-7853, 2214-7853
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Zusammenfassung:This study aimed to develop a smart model prediction of strain calculation using fiber optic sensors and neural network. Optical parameters are obtained experimentally on a cantilever beam structure, under static loading conditions. Five variations are used by creating external damage to study strain variations on healthy, single damage and multiple damage beam structures. The strain values were correlated to the set of phase difference and change in intensities by using feed-forward back propagation neural network approach. The strain values using optical parameters were verified with conventional strain gauge measurement and finite element analysis. The neural network simulation provides advance and more accurate correlation results with strain gauge and FEA analysis.
ISSN:2214-7853
2214-7853
DOI:10.1016/j.matpr.2020.01.178