prikaz prve stranice dokumenta Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash
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Scientific paper - Original scientific paper
Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash
Materials, 15 (2022), 12; 4191. https://doi.org/10.3390/ma15124191
Kovačević, Miljan; Lozančić, Silva; Nyarko, Emmanuel Karlo; Hadzima-Nyarko, Marijana

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Kovačević, M., Lozančić, S., Nyarko, E. K. & Hadzima Nyarko, M. (2022). Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash. Materials, 15. (12). doi: 10.3390/ma15124191

Kovačević, Miljan, et al. "Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash." Materials, vol. 15, no. 12, 2022. https://doi.org/10.3390/ma15124191

Kovačević, Miljan, Silva Lozančić, Emmanuel Karlo Nyarko and Marijana Hadzima Nyarko. "Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash." Materials 15, no. 12 (2022). https://doi.org/10.3390/ma15124191

Kovačević, M., et al. (2022) 'Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash', Materials, 15(12). doi: 10.3390/ma15124191

Kovačević M, Lozančić S, Nyarko EK, Hadzima Nyarko M. Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash. Materials [Internet]. 2022 [cited 2025 January 10];15(12). doi: 10.3390/ma15124191

M. Kovačević, S. Lozančić, E. K. Nyarko and M. Hadzima Nyarko, "Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash", Materials, vol. 15, no. 12, 2022. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:133:249835. [Accessed: 10 January 2025]

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Metadata
Title (english)Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash
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)
Replacing a specified quantity of cement with Class F fly ash contributes to sustainable development and reducing the greenhouse effect. In order to use Class F fly ash in self-compacting concrete (SCC), a prediction model that will give a satisfactory accuracy value for the compressive strength of such concrete is required. This paper considers a number of machine learning models created on a dataset of 327 experimentally tested samples in order to create an optimal predictive model. The set of input variables for all models consists of seven input variables, among which six are constituent components of SCC, and the seventh model variable represents the age of the sample. Models based on regression trees (RTs), Gaussian process regression (GPR), support vector regression (SVR) and artificial neural networks (ANNs) are considered. The accuracy of individual models and ensemble models are analyzed. The research shows that the model with the highest accuracy is an ensemble of ANNs. This accuracy expressed through the mean absolute error (MAE) and correlation coefficient (R) criteria is 4.37 MPa and 0.96, respectively. This paper also compares the accuracy of individual prediction models and determines their accuracy. Compared to the individual ANN model, the more transparent multi-gene genetic programming (MGPP) model and the individual regression tree (RT) model have comparable or better prediction accuracy. The accuracy of the MGGP and RT models expressed through the MAE and R criteria is 5.70 MPa and 0.93, and 6.64 MPa and 0.89, respectively.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleMaterials
Numberingvol. 15, no. 12, 4191
p-ISSN1996-1944
DOIhttps://doi.org/10.3390/ma15124191
URN:NBNurn:nbn:hr:133:249835
Publication2022
Document URLhttps://www.mdpi.com/1996-1944/15/12/4191
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-12-06 10:51:03