Geometric nonlinear analysis of stiffened prismatic shell structures using the compound strip method

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This paper shows that geometric nonlinear behaviour of stiffened prismatic shells can be successfully modelled with the compound strip method. Longitudinal and transverse beams are introduced into the finite strip method using strain energy through their interface lines. Advantages of the presented method are the low number of employed degrees of freedom and the semi-analytical approximation of the displacement field. This method also addresses one of the main drawbacks of the finite strip method, which is the inclusion of intermediate supports. All matrices are derived in a closed form and analytical solutions of five characteristic integrals are given. Numerical analysis shows that the compound strip method gives reasonably accurate results for complex equilibrium paths when compared with the finite element method. This method gives stiffer solutions for softening branch and more flexible results for hardening branch of the equilibrium path. Convergence of all variables is very fast except the longitudinal stresses in the shell above the transverse beam. Simple parallelization is applied and the computation time is significantly reduced.

Original languageEnglish
Title of host publicationProceedings of the 14th International Conference on Civil, Structural and Environmental Engineering Computing, CC 2013
PublisherCivil-Comp Press
ISBN (Print)9781905088577
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event14th International Conference on Civil, Structural and Environmental Engineering Computing: CC 2013 - Cagliari, Sardinia, Italy
Duration: 3 Sept 20136 Sept 2013


Conference14th International Conference on Civil, Structural and Environmental Engineering Computing
Abbreviated titleCC 2013
CityCagliari, Sardinia


  • Compound strip method
  • Geometric nonlinear analysis
  • Prismatic shells

ASJC Scopus subject areas

  • Environmental Engineering
  • Civil and Structural Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence

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