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Neural network-based prediction of the long-term time-dependent mechanical behavior of laminated composite plates with arbitrary hygrothermal effects.
- Source :
-
Journal of Mechanical Science & Technology . Oct2021, Vol. 35 Issue 10, p4643-4654. 12p. - Publication Year :
- 2021
-
Abstract
- Recurrent neural network (RNN)-based accelerated prediction was achieved for the long-term time-dependent behavior of viscoelastic composite laminated Mindlin plates subjected to arbitrary mechanical and hygrothermal loading. Time-integrated constitutive stress-strain relation was simplified via Laplace transform to a linear system to reduce the computational storage. A fast converging smooth finite element method named cell-based smoothed discrete shear gap was employed to enhance the data generation procedure for straining RNNs with a sparse mesh. This technique is applicable under varying hygrothermal conditions for real engineering structure problems with fluctuating temperature and moisture. Hence, accurate RNN-based long-term deformation prediction for laminated structures was realized using the history of environmental temperature and moisture condition. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1738494X
- Volume :
- 35
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Journal of Mechanical Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 153011127
- Full Text :
- https://doi.org/10.1007/s12206-021-0932-2