Steel and Composite Structures

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Data-driven detection of mooring failures in offshore floating photovoltaics using artificial neural networks

  • Jihun Song (School of Civil, Environmental, and Architectural Engineering, Korea University) ;
  • Yunhak Noh (School of Civil, Environmental, and Architectural Engineering, Korea University) ;
  • Hunhee Cho (School of Civil, Environmental, and Architectural Engineering, Korea University) ;
  • Goangseup Zi (School of Civil, Environmental, and Architectural Engineering, Korea University) ;
  • Seungjun Kim (School of Civil, Environmental, and Architectural Engineering, Korea University)
  • Received : 2024.05.16
  • Accepted : 2024.10.24
  • Published : 2024.12.10
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Abstract

The network theory studies interconnection between discrete objects to find about the behavior of a collection of objects. Also, nanomaterials are a collection of discrete atoms interconnected together to perform a specific task of mechanical or/and electrical type. Therefore, it is reasonable to use the network theory in the study of behavior of super-molecule in nanoscale. In the current study, we aim to examine vibrational behavior of spherical nanostructured composite with different geometrical and materials properties. In this regard, a specific shear deformation displacement theory, classical elasticity theory and analytical solution to find the natural frequency of the spherical nano-composite structure. The analytical results are validated by comparison to finite element (FE). Further, a detail comprehensive results of frequency variations are presented in terms of different parameters. It is revealed that the current methodology provides accurate results in comparison to FE results. On the other hand, different geometrical and weight fraction have influential role in determining frequency of the structure.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. RS-2021-NR060085).

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