Probabilistic stability analysis of functionally graded graphene reinforced porous beams

Kang Gao, Duy Minh Do, Ruilong Li, Sritawat Kitipornchai, Jie Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

71 Citations (Scopus)


This paper presents the first attempt to study the probabilistic stability characteristics of functionally graded (FG) graphene platelets (GPLs) reinforced beams by taking into account the multidimensional probability distributions, such as stochastic porosity and GPL distribution patterns as well as random material properties. For this purpose, a non-inclusive Chebyshev metamodel (CMM), which is implemented on deterministic analysis using discrete singular convolution (DSC) method with excellent computational efficiency and accuracy, is proposed and used to obtain both deterministic and probabilistic results including probability density functions (PDFs), cumulative density functions (CDFs), means and standard deviations of the critical buckling load. The present analysis is rigorously validated through direct comparisons against the results obtained by a direct quasi-Monte Carlo simulation (QMCS) method and those available in open literature. The influences of material properties, porosity distribution, GPL dispersion pattern and boundary condition on probabilistic buckling behaviour of the FG-GPL beam are comprehensively investigated. The global sensitivity analysis is also conducted. The results suggest that the critical buckling load of the FG-GPL beam is most sensitive to porosity distribution, followed by porosity coefficient and GPL weight fraction.

Original languageEnglish
Article number105738
JournalAerospace Science and Technology
Publication statusPublished - Mar 2020


  • Chebyshev metamodel
  • Functionally graded porous structures
  • Graphene platelet
  • Sensitivity analysis
  • Stochastic stability analysis

ASJC Scopus subject areas

  • Aerospace Engineering


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