Next Article in Journal
Bainitic Ferrite Plate Thickness Evolution in Two Nanostructured Steels
Next Article in Special Issue
Meta-Analysis and Machine Learning Models to Optimize the Efficiency of Self-Healing Capacity of Cementitious Material
Previous Article in Journal
Effect of Hydrothermal Treatment and Doping on the Microstructural Features of Sol-Gel Derived BaTiO3 Nanoparticles
Previous Article in Special Issue
Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature
Article

Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning

1
Faculty of Technical Sciences, University of Pristina, Knjaza Milosa 7, 38220 Kosovska Mitrovica, Serbia
2
Faculty of Civil Engineering, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000 Osijek, Croatia
3
Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2B, 31000 Osijek, Croatia
*
Authors to whom correspondence should be addressed.
Academic Editor: Łukasz Sadowski
Materials 2021, 14(15), 4346; https://doi.org/10.3390/ma14154346
Received: 9 July 2021 / Revised: 30 July 2021 / Accepted: 31 July 2021 / Published: 3 August 2021
(This article belongs to the Special Issue Artificial Intelligence for Cementitious Materials)
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. View Full-Text
Keywords: self-compacting rubberized concrete; compressive strength; machine learning; artificial neural networks; regression tree ensembles; support vector regression; Gaussian process regression self-compacting rubberized concrete; compressive strength; machine learning; artificial neural networks; regression tree ensembles; support vector regression; Gaussian process regression
Show Figures

Figure 1

MDPI and ACS Style

Kovačević, M.; Lozančić, S.; Nyarko, E.K.; Hadzima-Nyarko, M. Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning. Materials 2021, 14, 4346. https://doi.org/10.3390/ma14154346

AMA Style

Kovačević M, Lozančić S, Nyarko EK, Hadzima-Nyarko M. Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning. Materials. 2021; 14(15):4346. https://doi.org/10.3390/ma14154346

Chicago/Turabian Style

Kovačević, Miljan, Silva Lozančić, Emmanuel K. Nyarko, and Marijana Hadzima-Nyarko. 2021. "Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning" Materials 14, no. 15: 4346. https://doi.org/10.3390/ma14154346

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop