Journal of Materials Science

, Volume 42, Issue 16, pp 7016–7030 | Cite as

Statistical model for characterizing random microstructure of inclusion–matrix composites

  • Ahmed Al-OstazEmail author
  • Anipindi Diwakar
  • Khalid I. Alzebdeh


The variation of arrangement of micro-structural entities (i.e. inclusions) influences local properties of composites. Thus, there is a need to classify and quantify different micro-structural arrangements. In other words, it is necessary to identify descriptors that characterize the spatial dispersion of inclusions in random composites. On the other hand, Delaunay triangulation associated with an arbitrary set of points in a plane is unique which makes it a good candidate for generating such descriptors. This paper presents a framework for establishing a methodology for characterizing microstructure morphology in random composites and correlating that to local stress field. More specifically, in this paper we address three main issues: correlating microstructure morphology to local stress fields, effect of clustering of inclusions on statistical descriptors identified in the paper, and effect of number of realizations of statistical volume elements (SVEs) on statistical descriptors.


Representative Volume Element Delaunay Triangulation Parent Point Voronoi Cell Random Arrangement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to acknowledge the partial support for this research from U.S. Air Force Grant # F08637-03-C-6006 with a subcontract # S-29000.23 from Applied Research Associates Inc. Also, the authors would like to acknowledge the collaboration and help they received from Prof. Martin Ostoja-Starzewski.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Ahmed Al-Ostaz
    • 1
    Email author
  • Anipindi Diwakar
    • 2
  • Khalid I. Alzebdeh
    • 3
  1. 1.Department of Civil EngineeringUniversity of MississippiUniversityUSA
  2. 2.SC Solutions, Inc.SunnyvaleUSA
  3. 3.Department of Mechanical & Industrial EngineeringSultan Qaboos UniversityMuscatSultanate of Oman

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