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Computers and Concrete
  Volume 9, Number 5, April 2012, pages 375-387

Automatic categorization of chloride migration into concrete modified with CFBC ash
Maria Marks, Daria Jozwiak-Niedzwiedzka and Micha A. Glinicki

Abstract     [Full Text]
    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.
Key Words
    concrete durability; chloride ions migration; circulated fluidized bed combustion fly ash (cfbc fly ash); machine learning; classification rules; database.
Maria Marks, Daria Jo wiak-Nied wiedzka and Micha A. Glinicki: Institute of Fundamental Technological Research, Polish Academy of Sciences Pawi skiego 5 B, 02-106 Warszawa, Poland

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