Naslov
Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self- Compacting Concrete with Class F Fly Ash

Autori
Kovačević, Miljan ; Lozančić, Silva ; Nyarko, Emmanuel Karlo ; Hadzima-Nyarko, Marijana

Izvornik
Materials (1996-1944) 15 (2022), 12; 4191, 32

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
self-compacting concrete ; Class F fly ash ; compressive strength ; machine learning ; artificial neural networks ; regression trees ; Gaussian process regression

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo, Računarstvo, Temeljne tehničke znanosti

POVEZANOST RADA