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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| Vydané v: | Materials today : proceedings Ročník 21; s. 1380 - 1395 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
2020
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| ISSN: | 2214-7853, 2214-7853 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Tyagi, Amit Wali, Ashwarya Sheel |
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| CitedBy_id | crossref_primary_10_1016_j_gsf_2020_11_005 crossref_primary_10_1016_j_radphyschem_2022_110558 crossref_primary_10_1140_epjp_s13360_022_02815_3 crossref_primary_10_1016_j_chemosphere_2023_137788 crossref_primary_10_3390_electronics13142789 crossref_primary_10_3390_en16104162 crossref_primary_10_1080_10916466_2022_2098327 crossref_primary_10_1155_2021_2922728 crossref_primary_10_1016_j_jngse_2022_104468 crossref_primary_10_3390_w15193380 crossref_primary_10_3390_su13179537 |
| Cites_doi | 10.3390/s120303314 10.24237/djes.2015.08103 10.1007/BF02703781 10.1364/AO.17.002867 10.1016/j.engstruct.2005.09.018 10.1139/P09-062 10.1088/0964-1726/9/6/305 10.1016/j.measurement.2008.05.005 10.1016/S0143-8166(00)00033-6 10.1016/j.measurement.2017.08.047 10.1088/0957-0233/14/8/329 |
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| SubjectTerms | Finite element analysis (FEA) Neural network Optical parameters Strain |
| Title | Comparative Study of Advance Smart Strain Approximation Method Using Levenberg-Marquardt and Bayesian Regularization Backpropagation Algorithm |
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