Thermomechanical in-plane dynamic instability of asymmetric restrained functionally graded graphene reinforced composite arches via machine learning-based models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8194
- @Article{YANG:2023:compstruct,
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author = "Zhicheng Yang and Shaoyu Zhao and Jie Yang and
Airong Liu and Jiyang Fu",
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title = "Thermomechanical in-plane dynamic instability of
asymmetric restrained functionally graded graphene
reinforced composite arches via machine learning-based
models",
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journal = "Composite Structures",
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year = "2023",
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volume = "308",
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pages = "116709",
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keywords = "genetic algorithms, genetic programming, Defective
graphene, Functionally graded arch, Asymmetric elastic
constraint, Dynamic instability, Thermomechanical
action, Genetic programming assisted micromechanical
model",
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URL = "https://www.sciencedirect.com/science/article/pii/S0263822323000533",
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ISSN = "0263-8223",
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DOI = "doi:10.1016/j.compstruct.2023.116709",
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abstract = "This paper studies the thermomechanical in-plane
dynamic instability of asymmetric restrained
functionally graded graphene reinforced composite
(FG-GRC) arches, where graphene sheets with atom
vacancy defects are distributed along the arch
thickness according to a power law distribution. The
temperature-dependent mechanical properties of the
graphene reinforced composites are determined by a
genetic programming (GP) assisted micromechanical
model. The governing equations for the thermomechanical
in-plane dynamic instability are derived by Hamilton's
principle and solved by differential quadrature method
(DQM) in conjunction with Bolotin method. Comprehensive
numerical studies are performed to examine the effects
of vacancy defect and graded distribution of graphene,
temperature variation, load position, as well as
boundary conditions on the free vibration, elastic
buckling, and dynamic instability behaviors of the
FG-GRC arch. Numerical results show that the structural
performance of the FG-GRC arch is weakened by graphene
defect and temperature rise and is significantly
influenced by both graphene distribution and boundary
conditions",
- }
Genetic Programming entries for
Zhicheng Yang
Shaoyu Zhao
Jie Yang
Airong Liu
Jiyang Fu
Citations