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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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. i 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, br. 12, 2022. https://doi.org/10.3390/ma15124191

Kovačević, Miljan, Silva Lozančić, Emmanuel Karlo Nyarko i Marijana Hadzima Nyarko. "Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash." Materials 15, br. 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. [pristupljeno 22.01.2025.];15(12). doi: 10.3390/ma15124191

M. Kovačević, S. Lozančić, E. K. Nyarko i 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, br. 12, 2022. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:133:249835. [Citirano: 22.01.2025.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:133:249835
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Naslov (engleski)Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash
AutorMiljan Kovačević
AutorSilva Lozančić
AutorEmmanuel Karlo Nyarko
AutorMarijana Hadzima-Nyarko
Autorova ustanovaSveučilište Josipa Jurja Strossmayera u Osijeku
Građevinski i arhitektonski fakultet Osijek
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Građevinarstvo
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaMaterials
Brojčani podacivol. 15, br. 12, 4191
p-ISSN1996-1944
DOIhttps://doi.org/10.3390/ma15124191
URN:NBNurn:nbn:hr:133:249835
Datum objave publikacije2022
URL dokumentahttps://www.mdpi.com/1996-1944/15/12/4191
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2024-12-06 10:51:03