prikaz prve stranice dokumenta Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning
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Scientific paper - Original scientific paper
Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning
Materials,, 14 (2021), 15; 4346. https://doi.org/10.3390/ma14154346
Kovačević, Miljan; Lozančić, Silva; Nyarko, Emmanuel Karlo; Hadzima-Nyarko, Marijana

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Kovačević, M., Lozančić, S., Nyarko, E. K. & Hadzima Nyarko, M. (2021). Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning. Materials,, 14. (15). doi: 10.3390/ma14154346

Kovačević, Miljan, et al. "Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning." Materials,, vol. 14, no. 15, 2021. https://doi.org/10.3390/ma14154346

Kovačević, Miljan, Silva Lozančić, Emmanuel Karlo Nyarko and Marijana Hadzima Nyarko. "Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning." Materials, 14, no. 15 (2021). https://doi.org/10.3390/ma14154346

Kovačević, M., et al. (2021) 'Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning', Materials,, 14(15). doi: 10.3390/ma14154346

Kovačević M, Lozančić S, Nyarko EK, Hadzima Nyarko M. Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning. Materials, [Internet]. 2021 [cited 2025 January 10];14(15). doi: 10.3390/ma14154346

M. Kovačević, S. Lozančić, E. K. Nyarko and M. Hadzima Nyarko, "Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning", Materials,, vol. 14, no. 15, 2021. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:133:494654. [Accessed: 10 January 2025]

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Metadata
Title (english)Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning
AuthorMiljan Kovačević
AuthorSilva Lozančić
AuthorEmmanuel Karlo Nyarko
AuthorMarijana Hadzima-Nyarko
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)
This paper gives a comprehensive overview of the state-of-the-art machine learning methods that can be used for estimating self-compacting rubberized concrete (SCRC) compressive strength, including multilayered perceptron artificial neural network (MLP-ANN), ensembles of MLP-ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) and Gaussian process regression (GPR). As a basis for the development of the forecast model, a database was obtained from an experimental study containing a total of 166 samples of SCRC. Ensembles of MLP-ANNs showed the best performance in forecasting with a mean absolute error (MAE) of 2.81 MPa and Pearson’s linear correlation coefficient (R) of 0.96. The significantly simpler GPR model had almost the same accuracy criterion values as the most accurate model ; furthermore, feature reduction is easy to combine with GPR using automatic relevance determination (ARD), leading to models with better performance and lower complexity.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleMaterials,
Numberingvol. 14, no. 15, 4346
e-ISSN1996-1944
DOIhttps://doi.org/10.3390/ma14154346
URN:NBNurn:nbn:hr:133:494654
Publication2021
Document URLhttps://www.mdpi.com/1996-1944/14/15/4346
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-12-05 14:29:51