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Automatic categorization of chloride migration into concrete modified with CFBC ash
Maria Marks,Daria Józwiak-Niedzwiedzka,Micha A. Glinicki 사단법인 한국계산역학회 2012 Computers and Concrete, An International Journal Vol.9 No.5
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.
Assessment of neutron-induced activation of irradiated samples in a research reactor
Harsányi Ildikó,Horváth András,Kis Zoltán,Gméling Katalin,Jozwiak-Niedzwiedzka Daria,Glinicki Michal A.,Szentmiklósi László 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.3
The combination of MCNP6 and the FISPACT codes was used to predict inventories of radioisotopes produced by neutron exposure of a sample in a research reactor. The detailed MCNP6 model of the Budapest Research Reactor and the specific irradiation geometry of the NAA channel was established, while realistic material cards were specified based on concentrations measured by PGAA and NAA, considering the precursor elements of all significant radioisotopes. The energy- and spatial distributions of the neutron field calculated by MCNP6 were transferred to FISPACT, and the resulting activities were validated against those measured using neutron-irradiated small and bulky targets. This approach is general enough to handle different target materials, shapes, and irradiation conditions. A general agreement within 10% has been achieved. Moreover, the method can also be made applicable to predict the activation properties of the near-vessel concrete of existing nuclear installations or assist in the optimal construction of new nuclear power plant units.