Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
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Shape Optimization of Shell Structures using Visual Programming

비주얼 프로그래밍에 기반한 쉘 구조물의 형상최적화

  • Lee, Sang-Jin (ADOPT Research Group, Department of Architectural Engineering, Gyeongsnag National University)
  • 이상진 (경상국립대학교 건축공학과)
  • Received : 2021.10.16
  • Accepted : 2021.11.16
  • Published : 2021.12.30
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Abstract

This study proposes a new technique for shape optimization of shell structures using visual programming. It aims to demonstrate the possibility and capability of visual programming techniques for shell shape optimization. The visual programming is achieved in the grasshopper environment. For the shape optimization process, the NURBS (Non-Uniform Rational B-spline) definition is introduced to represent the geometry of shell structure. The response of shell structure is evaluated by using OpenSees via the plug-in Alpaca4d. The optimization is then performed by using the plug-in NM-opti. All the process is utilized with the generic components of the grasshopper and two specific plug-ins. The present visual programming technique can achieve better optimization results than the reference solutions and provide similar optimum shapes from numerical results.

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References

  1. Arora, J.S. (2012). Introduction to optimum design, 3rd ed., New York: Academic Press.
  2. De Boor, C. (1978). A Practical Guide to Splines, Springer.
  3. Dorigo, M, & Stutzle, T. (2004). Ant Colony Optimization, A Braford Book.
  4. Dvorkin, E. N., & Bathe, K. J. (1984). A continuum mechanics based four-node shell element for general nonlinear analysis, Engineering Computations, 1, 77-88. https://doi.org/10.1108/eb023562
  5. Giraldo, J. M. G, & Palacio, L. G. (2020). The fourth industrial revolution, an opportunity for Civil Engineering, 15th Iberian Conference on Information Systems and Technologies (CISTI).
  6. Gregson, S. (2018). Nelder-Mead optimisation component in Grasshopper, Manual, Eckersley O'Callaghan.
  7. Haftka, R.T., & Gurdal Z. (1992). Element of structural optimization. New York: Kluwer Academic Publishers.
  8. Lee, S.J. (1999). A study on the shape and thickness optimization of shells using CAGD through minimization of strain energy with volume constraint, J. Compu. Struc. Eng. Institute of Korea, 12(4), 551-561.
  9. Lee. S.J. (2014). Finite element method with MATLAB, SJ Mirae.
  10. Lee. S.J. (2019). Truss Size Optimization with Frequency Constraints using ACO Algorithm, Journal of the Architectural Institute of Korea, 35(10), 135-142.
  11. Lee, S.J., & Kim. H.R. (2006). A study on the shape and thickenss optimization techniques for controlling natural frequency of shell structure, Journal of the Architectural Institute of Korea, 22(4), 73-82.
  12. Lee, S.J., & Bae, J.E. (2020). Multi-Objective Structural Optimization using Particle Swam Optimization, Journal of the Architectural Institute of Korea, 36(11), 281-288. https://doi.org/10.5659/JAIK.2020.36.11.281
  13. Mazzoni, S., McKenna, F., Scott, M. H., & Fenves, G. L. (2006). Open System for Earthquake Engineering Simulation (OpenSeess), Command Language Manual, Pacific Earthquake Engineering Research Center, University of California, Berkeley.
  14. ModeLab (2015). The dynamo primer, Autodesk.
  15. Myers, B.A. (2013). Graphical user interface programming, the CRC handbook of computer science and engineering.
  16. Pellegrino, M., & Gaudioso, D. (2021). Alpaca4D for Rhino 7: Grasshopper plugin for OpenSees.
  17. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Downhill Simplex Method in Multidimensions, in Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University Press.
  18. Robert McNeel, & Associates, The Grasshopper primer, ModeLab.
  19. Sivanandam, S.N. & Deepa, S.N. (2008). Introduction to genetic algorithms, Springer.
  20. Smith, I.M. & Griffiths, D.V. (1998). Programming the finite element method, John Wiley & Sons.