prikaz prve stranice dokumenta Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder
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Scientific paper - Original scientific paper
Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder
Scientific Reports, 12 (2022); 13242. https://doi.org/10.1038/s41598-022-17670-6
Czarnecki, Slawomir; Hadzima-Nyarko, Marijana; Chajec, Adrian; Sadowski, Lukasz

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Czarnecki, S., Hadzima Nyarko, M., Chajec, A. & Sadowski, L. (2022). Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder. Scientific Reports, 12.. doi: 10.1038/s41598-022-17670-6

Czarnecki, Slawomir, et al. "Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder." Scientific Reports, vol. 12, 2022. https://doi.org/10.1038/s41598-022-17670-6

Czarnecki, Slawomir, Marijana Hadzima Nyarko, Adrian Chajec and Lukasz Sadowski. "Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder." Scientific Reports 12 (2022). https://doi.org/10.1038/s41598-022-17670-6

Czarnecki, S., et al. (2022) 'Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder', Scientific Reports, 12. doi: 10.1038/s41598-022-17670-6

Czarnecki S, Hadzima Nyarko M, Chajec A, Sadowski L. Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder. Scientific Reports [Internet]. 2022 [cited 2025 January 14];12. doi: 10.1038/s41598-022-17670-6

S. Czarnecki, M. Hadzima Nyarko, A. Chajec and L. Sadowski, "Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder", Scientific Reports, vol. 12, 2022. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:133:638977. [Accessed: 14 January 2025]

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Metadata
Title (english)Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder
AuthorSlawomir Czarnecki
AuthorMarijana Hadzima-Nyarko
AuthorAdrian Chajec
AuthorLukasz Sadowski
Author's institutionJosip Juraj Strossmayer University of Osijek
Faculty of Civil Engineering and Architecture Osijek
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Abstract (english)
In this study, a machine learning model for the precise manufacturing of green cementitious composites modified with granite powder sourced from quarry waste was designed. For this purpose, decision tree, random forest and AdaBoost ensemble models were used and compared. A database was created containing 216 sets of data based on an experimental study. The database consists of parameters such as the percentage of cement substituted with granite powder, time of testing and curing conditions. It was shown that this method for designing green cementitious composite mixes, in terms of predicting compressive strength using ensemble models and only three input parameters, can be more accurate and much more precise than the conventional approach. Moreover, to the best of the authors’ knowledge, artificial intelligence has been one of the most effective and precise methods used in the design and manufacturing industry in recent decades. The simplicity of this method makes it more suitable for construction practice due to the ease of evaluating the input variables. As the push towards decreasing carbon emissions increases, a method for designing green cementitious composites without producing waste that is more precise than traditional tests performed in a laboratory is essential.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleScientific Reports
Numberingvol. 12, 13242
p-ISSN2045-2322
DOIhttps://doi.org/10.1038/s41598-022-17670-6
URN:NBNurn:nbn:hr:133:638977
Publication2022
Document URLhttps://www.nature.com/articles/s41598-022-17670-6
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-12-06 09:23:45