Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks

  • Dieu T. T. Do Duy Tan Research Institute for Computational Engineering, Duy Tan University, 254 Nguyen Van Linh street, Da Nang city, Vietnam
  • Tan-Tien Nguyen Faculty of Engineering, Vietnamese-German University, Le Lai Street, Thu Dau Mot City, Binh Duong province, Vietnam
  • Quoc-Hung Nguyen Faculty of Engineering, Vietnamese-German University, Le Lai Street, Thu Dau Mot City, Binh Duong province, Vietnam
  • Tinh Quoc Bui Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, Japan
Keywords: artificial neural network, hexagonal cross-section, functionally graded materials, thin-walled beam

Abstract

We study static mechanical behavior of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using a non-traditional computational approach based on artificial neural network. One of the main objectives of our approach is to save the computational cost for the optimization process, which is usually time-consuming by using traditional methods such as finite element method (FEM). In this study, 1000 data sets randomly generated by the FEM through iterations are used for the training process to get optimal weights. Based on these obtained optimal weights, beam behaviors under the changes in material distribution through thickness could then be predicted. In this model, the ANN's inputs are the gradation index of the power-law distribution and thickness, while the outputs are compliance and beam displacements. The computed results are verified against those derived from the FEM.

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Published
16-08-2021
How to Cite
Do, D. T. T., Nguyen, T.-T., Nguyen, Q.-H., & Bui, T. Q. (2021). Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks. Journal of Science and Technology in Civil Engineering (STCE) - HUCE, 15(3), 1-12. https://doi.org/10.31814/stce.nuce2021-15(3)-01
Section
Research Papers