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Journal of Materials Science

, Volume 51, Issue 8, pp 3772–3783 | Cite as

Methods for fibre orientation analysis of X-ray tomography images of steel fibre reinforced concrete (SFRC)

  • Heiko Herrmann
  • Emiliano Pastorelli
  • Aki Kallonen
  • Jussi-Petteri Suuronen
Original Paper

Abstract

One of the most important factors to determine the mechanical properties of a fibre composite material is the orientation of the fibres in the matrix. This paper presents Hessian matrix-based algorithms to retrieve the orientation of individual fibres out of steel fibre reinforced cementitious composites samples scanned with an X-ray computed tomography scanner. The software implemented with the algorithms includes a massive data filtering component to remove noise from the data-sets and prepare them correctly for the analysis. Due to its short computational times and limited need for user intervention, the software is able to process and analyse large batches of data in short periods and provide results in a variety of visual and numerical formats. The application and comparison of these algorithms lead to further insight into the material behaviour. In contrast to the usual assumption that the fibres act only along their main axis, it is shown that the contribution of hooked-end fibres in other directions may be noticeable. This means that fibres, depending on their shape, should act as orthotropic inclusions. The methods can be used by research laboratories and companies on an everyday basis to obtain fibre orientations from samples, which in turn can be used in research, to study stress–strain behaviour, as input to constitutive models or for quality assurance.

Keywords

Fibre Orientation Steel Fibre Orientation Distribution Function Individual Fibre Steel Fibre Reinforce Concrete 
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.

Notes

Acknowledgements

This study was compiled with the assistance of the Tiger University Program of the Estonian Information Technology Foundation (VisPar system, EITSA/HITSA Tiigriülikool Grants 10-03-00-24, 12-03-00-11, 13030009 and travel Grants to present at VARE 2013 for E.P. and at SalentoAVR 2014 for H.H.). This research was supported by the European Union through the European Regional Development Fund, in particular through funding for the “Centre for Nonlinear Studies” as an Estonian national centre of excellence. This research was supported by the European Social Fund’s Doctoral Studies and Internationalisation Program DoRa T4 and the Doctoral School in Information and Communication Technology, which are carried out by Archimedes Foundation (scholarship for E.P.).

Author contributions

All authors contributed to the research and this article. In particular, HH designed the new algorithm and performed statistics, EP implemented the algorithm and filtering, JS and AK performed the tomography scanning and volume reconstruction.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Heiko Herrmann
    • 1
    • 2
  • Emiliano Pastorelli
    • 1
  • Aki Kallonen
    • 3
  • Jussi-Petteri Suuronen
    • 3
    • 4
  1. 1.Centre for Nonlinear StudiesInstitute of Cybernetics at Tallinn University of TechnologyTallinnEstonia
  2. 2.Institute of PhysicsTechnische Universität ChemnitzChemnitzGermany
  3. 3.Department of PhysicsUniversity of HelsinkiHelsinkiFinland
  4. 4.ESRF - The European SynchrotronGrenoble Cedex 9France

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