Computers and Concrete

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Automatic categorization of chloride migration into concrete modified with CFBC ash

  • Marks, Maria (Institute of Fundamental Technological Research, Polish Academy of Sciences) ;
  • Jozwiak-Niedzwiedzka, Daria (Institute of Fundamental Technological Research, Polish Academy of Sciences) ;
  • Glinicki, Michal A. (Institute of Fundamental Technological Research, Polish Academy of Sciences)
  • Received : 2010.01.13
  • Accepted : 2012.01.05
  • Published : 2012.05.25
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Abstract

The objective of this investigation was to develop rules for automatic categorization of concrete quality using selected artificial intelligence methods based on machine learning. The range of tested materials included concrete containing a new waste material - solid residue from coal combustion in fluidized bed boilers (CFBC fly ash) used as additive. The rapid chloride permeability test - Nordtest Method BUILD 492 method was used for determining chloride ions penetration in concrete. Performed experimental tests on obtained chloride migration provided data for learning and testing of rules discovered by machine learning techniques. It has been found that machine learning is a tool which can be applied to determine concrete durability. The rules generated by computer programs AQ21 and WEKA using J48 algorithm provided means for adequate categorization of plain concrete and concrete modified with CFBC fly ash as materials of good and acceptable resistance to chloride penetration.

Keywords

References

  1. Alterman, D. and Kasperkiewicz, J. (2006), "Evaluating concrete materials by application of automatic reasoning", Bull. Pol. Acad. Sci. Tech., 54(4), 353-361.
  2. Antoni, A., Horiguchi, T. and Saeki, N. (2005), "Chloride penetration into fiber reinforced concrete under static and cyclic compressive loading", Proceedings of 10 DBMC International Conference on Durability of Building Materials and Components, Lyon, April.
  3. Fu, X., Li, Q., Zhai, J., Sheng, G. and Li, F. (2008), "The physical-chemical characterization of mechanicallytreated CFBC fly ash", Cement Concrete Comp., 30(3), 220-226. https://doi.org/10.1016/j.cemconcomp.2007.08.006
  4. Giergiczny, Z. and Pu ak, T. (2008), "Properties of concrete with fluidal fly ash addition", Proceedings of 3rd International Symposium Non-Traditional Cement Concrete, 263-271, Brno, June.
  5. Glinicki, M.A. and Zielinski, M. (2009), "Frost salt scaling resistance of concrete containing CFBC fly ash", Mater. Struct., 42(7), 993-1002. https://doi.org/10.1617/s11527-008-9438-y
  6. Jo wiak-Nied wiedzka, D. (2009), "Effect of fluidized bed combustion fly ash on the chloride resistance and scaling resistance of concrete", Proceedings of RILEM Conference - Concrete in Aggressive Aqueous Environments, Performance, Testing and Modeling, 556-563, Toulouse, June.
  7. Kasperkiewicz, J. and Alterman, D. (2007), "Holistic approach to diagnostics of engineering materials", Comput. Assist. Mech. Eng. Sci., 14(2), 197-207.
  8. Krawiec, K. and Stefanowski, J. (2003), Machine learning and neural networks (in Polish), Poznan University of Technology, Poland.
  9. Melhem, H.G. and Cheng, Y. (2003), "Prediction of remaining service life of bridge decks using machine learning", J. Comput. Civil Eng., 17(1), 1-9. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:1(1)
  10. Ma olepszy, J. and Ko odziej, . (2009), "Resistance of cements with high amount of ashes from fluidized bed furnace to chloride diffusion", Proceedings of 5th International Conference Concrete and Concrete Structures, Zilina, October.
  11. Marks, M., Jó wiak-Nied wiedzka, D. and Glinicki, M.A. (2009), "Application of machine learning for prediction of concrete resistance to migration of chlorides", Proceedings of 9th International Symposium on Brittle Matrix Composites BMC9, 227-236, Warsaw, October.
  12. NT BUILD 492 (1999), Concrete, mortar and cement-based repair materials: chloride migration coefficient from non-steady-state migration experiments, Nordtest Method 492, Finland.
  13. Nowak, W. (2003), "Clean coal fluidized-bed technology in Poland", Appl. Energ, 74(3-4), 405-413. https://doi.org/10.1016/S0306-2619(02)00195-2
  14. Tang, L. (1996), Chloride transport in concrete - measurement and prediction, PhD thesis, Department of Building Materials, Chalmers University of Technology, Goteborg, Sweden.
  15. Sengul, O., Tasdemir, C. and Tasdemir, M.A. (2005), "Mechanical properties and rapid chloride permeability of concretes with ground fly ash", ACI Mater. J., 102(6), 414-421.
  16. Witten, I.H. and Frank, E. (2005), Data mining. Practical machine learning tools and techniques, Elsevier, San Francisco, CA.
  17. Wojtusiak, J. (2004), AQ21 user's guide, Reports of the Machine Learning and Inference Laboratory, MLI 04-3, George Mason University.

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