Computers and Concrete

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Fast classification of fibres for concrete based on multivariate statistics

  • Zarzycki, Pawel K. (Department of Environmental Technologies and Bioanalytics, Faculty of Civil Engineering, Environmental and Geodetic Sciences, Koszalin University of Technology) ;
  • Katzer, Jacek (Department of Construction and Building Materials, Faculty of Civil Engineering, Environmental and Geodetic Sciences, Koszalin University of Technology) ;
  • Domski, Jacek (Department of Concrete Structures and Technology of Concrete, Faculty of Civil Engineering, Environmental and Geodetic Sciences, Koszalin University of Technology)
  • Received : 2016.08.25
  • Accepted : 2017.05.11
  • Published : 2017.07.25
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Abstract

In this study engineered steel fibres used as reinforcement for concrete were characterized by number of key mechanical and spatial parameters, which are easy to measure and quantify. Such commonly used parameters as length, diameter, fibre intrinsic efficiency ratio (FIER), hook geometry, tensile strength and ductility were considered. Effective classification of various fibres was demonstrated using simple multivariate computations involving principal component analysis (PCA). Contrary to univariate data mining approach, the proposed analysis can be efficiently adapted for fast, robust and direct classification of engineered steel fibres. The results have revealed that in case of particular spatial/geometrical conditions of steel fibres investigated the FIER parameter can be efficiently replaced by a simple aspect ratio. There is also a need of finding new parameters describing properties of steel fibre more precisely.

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References

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