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Original scientific paper

https://doi.org/10.17559/TV-20210925112341

Gaussian Regression Process for Prediction of Compressive Strength of Thermally Activated Geopolymer Mortars

Nenad Ristić orcid id orcid.org/0000-0002-8201-892X ; University of Nis, Faculty of Civil Engineering and Architecture, Aleksandra Medvedeva 14 street, 18000, Nis, Serbia
Emina Petrović orcid id orcid.org/0000-0002-4230-2416 ; Faculty of Mechanical Engineering, Aleksandra Medvedeva 14 street, 18000, Nis, Serbia
Jelena Bijeljić ; Academy of technical and educational vocational studies Niš, Aleksandra Medvedeva 20 street, 18000, Nis, Serbia
Miloš Simonović ; Faculty of Mechanical Engineering, Aleksandra Medvedeva 14 street, 18000, Nis, Serbia
Dušan Grdić orcid id orcid.org/0000-0002-2651-7388 ; University of Nis, Faculty of Civil Engineering and Architecture, Aleksandra Medvedeva 14 street, 18000, Nis, Serbia
Vlastimir Nikolić ; Faculty of Mechanical Engineering, Aleksandra Medvedeva 14 street, 18000, Nis, Serbia
Zoran Grdić ; University of Nis, Faculty of Civil Engineering and Architecture, Aleksandra Medvedeva 14 street, 18000, Nis, Serbia


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Abstract

The primary objective of this research is the development of a prediction model of the compressive strength of geopolymer mortars made with fly ash and granular slag which hardened in different curing conditions. Data for the numerical analysis were obtained by experimental research; for this purpose 45 series of geopolymer mortars were made, 9 of which were cured in ambient conditions at a temperature of 22 °С, and the remaining were exposed to thermal activation for a duration of 24 h at the temperatures of 65 °С, 75 °С, 85 °С and 95 °С. Using machine learning, a Gaussian regression method was developed in which the curing temperature and the percentage mass content of fly ash and granular slag were used as input parameters, and the compressive strength as the output. Based on the results of the developed model, it can be concluded that the Gaussian regression process can be used as a reliable regression method for predicting the compressive strength of geopolymer mortars based on fly ash and granular slag.

Keywords

curing temperature, fly ash, Gaussian regression process, geopolymer mortars, ground granulated blast furnace slag, machine learning

Hrčak ID:

284896

URI

https://hrcak.srce.hr/284896

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