prikaz prve stranice dokumenta Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning
  Preuzmi
PDF 40.29 MB
Znanstveni rad - Izvorni znanstveni rad
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

Citirajte ovaj rad

Kovačević, M., Lozančić, S., Nyarko, E. K. i 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, br. 15, 2021. https://doi.org/10.3390/ma14154346

Kovačević, Miljan, Silva Lozančić, Emmanuel Karlo Nyarko i Marijana Hadzima Nyarko. "Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning." Materials, 14, br. 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. [pristupljeno 14.01.2025.];14(15). doi: 10.3390/ma14154346

M. Kovačević, S. Lozančić, E. K. Nyarko i M. Hadzima Nyarko, "Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning", Materials,, vol. 14, br. 15, 2021. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:133:494654. [Citirano: 14.01.2025.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:133:494654
Prijavite se u repozitorij kako biste mogli spremiti objekt u svoju listu.
Podaci o radu
Naslov (engleski)Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning
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)
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.
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. 14, br. 15, 4346
e-ISSN1996-1944
DOIhttps://doi.org/10.3390/ma14154346
URN:NBNurn:nbn:hr:133:494654
Datum objave publikacije2021
URL dokumentahttps://www.mdpi.com/1996-1944/14/15/4346
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2024-12-05 14:29:51