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Evaluation of the Utility of Using Classification Algorithms when Designing New Polymer Composites

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Języki publikacji
EN
Abstrakty
EN
Polymer composites are the materials that can be successfully used in the places where high mechanical strength and chemical resistance as well as low absorbability are required. These unique features of polymer composites are obtained mainly due to a suitably selected binder, i.e. a synthetic resin. At the same time, this component accounts for the high production costs of these materials. Partial substitution of the resin with glycolisates obtained using poly(ethylene terephthalate) waste (PET), helps reduce the price of polymeric mortars, while maintaining favourable physicomechanical properties. This modification method also has a beneficial effect on the environment, as it allows the utilisation of a very common waste, which is difficult to dispose of. The article concerns three types of resin mortars, i.e. epoxy, polyester and polyester with the addition of colloidal silica, modified with PET glycolisate. On the basis of the obtained data set and database knowledge mining techniques, such as discriminant analysis and decision trees, it was shown to what extent the type of resin and the presence of an added modifier differentiate the mortar properties. The results obtained with both methods were compared. It was confirmed that these techniques are effective both in the classification and prediction of the type (selection) of mortar in the process of designing new composites.
Rocznik
Strony
212--225
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
  • Rzeszow University of Technology, The Faculty of Civil and Environmental Engineering and Architecture, ul. Poznańska 2, 35-084 Rzeszów, Poland
  • Rzeszow University of Technology, The Faculty of Chemistry, ul. Powstańców Warszawy 6, 35-959 Rzeszów, Poland
  • Rzeszow University of Technology, The Faculty of Civil and Environmental Engineering and Architecture, ul. Poznańska 2, 35-084 Rzeszów, Poland
Bibliografia
  • 1. Czarnecki L. 2010. Polymer concretes. Cement Lime Concrete, 2, 63–85.
  • 2. Lokuge W., Aravinthan T. 2013. Effect of fly ash on the behaviour of polymer concrete with different types of resin. Materials and Design, 51, 175–181.
  • 3. Dębska B., Lichołai L. 2016. The effect of the type of curing agent on selected properties of epoxy mortar modified with PET glycolisate. Construction and Building Materials, 124, 11–19.
  • 4. Dębska B., Lichołai L. 2016. Resin Composites with High Chemical Resistance for Application in Civil Engineering. Periodica Polytechnica-Civil Engineering, 60(2), 281–287.
  • 5. Radkiewicz P. 2010. Analiza dyskryminacyjna. Podstawowe założenia i zastosowania w badaniach społecznych. Psychologia Społeczna, 2–3(14), 142–161 (in Polish).
  • 6. Quinlan J. R. 1986. Induction of Decision Trees. Machine Learning, 1, 81–106, Kluwer Academic Publishers. http://hunch.net/~coms-4771/quinlan.pdf. (Accessed 12 July 2019).
  • 7. Rokach L., Maimon O. 2008. Data mining with decision trees: theory and applications. World Scientific Pub Co Inc.
  • 8. Feldesman M. R. 2002. Classification Trees as an Alternative to Linear Discriminant Analysis. American Journal of Physical Anthropology, 119, 257–275.
  • 9. Yong L. 2006. Predicting materials properties and behavior using classification and regression trees. Materials Science and Engineering: A, 433, 261–268.
  • 10. Hajigholizadeh M., Melesse A. M. 2017. Assortment and spatiotemporal analysis of surface water quality using cluster and discriminant analyses. Catena, 151, 247–258.
  • 11. Dębska B. J., Guzowska-Świder B. 2011. Decision Trees in Selection of Featured Determined Food Quality. Analytica Chimica Acta, 705, 261–271.
  • 12. Oro S. R., Neto A. Ch., Mafioleti T. R., Ribeiro Pardo Garcia S., Neumann Júnior C. 2016. Multivariate analysis of the displacements of a concrete dam with respect to the action of environmental conditions. Independent Journal of Management and Production, 7, 526–545.
  • 13. Vítková G., Prokeš L., Novotný K., Pořízka P., Novotný J., Všianský D., Čelko L., Kaiser J. 2014. Comparative study on fast classification of brick samples by combination of principal component analysis and linear discriminant analysis using stand-off and table-top laser-induced breakdown spectroscopy. Spectrochimica Acta Part B, 101, 191–199.
  • 14. Dębska B. 2018. The use of discriminant analysis methods for diagnosis of the causes of differences in the properties of resin mortar containing various fillers. E3S Web of Conferences, 00017, 49, 1–10. https://doi.org/10.1051/e3sconf/20184900017.
  • 15. Moczko J. 2003. Wybrane metody eksploracji danych i wspomagania procesów decyzyjnych (Selected methods of data mining and supporting decision-making processes) StatSoft Polska, 5–21. https://media.statsoft.pl/_old_dnn/downloads/moczko3.pdf. (Accessed 12 July 2019) (in Polish).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b5f5c88b-4ce0-4148-a042-7346e527552a
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