Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Green Concretes with Rice Husk Ash

Miljan Kovačević (), Marijana Hadzima-Nyarko, Ivanka Netinger Grubeša, Dorin Radu and Silva Lozančić
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Miljan Kovačević: Faculty of Technical Sciences, University of Pristina, Knjaza Milosa 7, 38220 Kosovska Mitrovica, Serbia
Marijana Hadzima-Nyarko: Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000 Osijek, Croatia
Ivanka Netinger Grubeša: Department of Construction, University North, 104. Brigade 3, 42000 Varaždin, Croatia
Dorin Radu: Faculty of Civil Engineering, Transilvania University of Brașov, 500152 Brașov, Romania
Silva Lozančić: Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000 Osijek, Croatia

Mathematics, 2023, vol. 12, issue 1, 1-25

Abstract: To promote sustainable growth and minimize the greenhouse effect, rice husk fly ash can be used instead of a certain amount of cement. The research models the effects of using rice fly ash as a substitute for regular Portland cement on the compressive strength of concrete. In this study, different machine-learning techniques are investigated and a procedure to determine the optimal model is provided. A database of 909 analyzed samples forms the basis for creating forecast models. The derived models are assessed using the accuracy criteria RMSE, MAE, MAPE, and R. The research shows that artificial intelligence techniques can be used to model the compressive strength of concrete with acceptable accuracy. It is also possible to evaluate the importance of specific input variables and their influence on the strength of such concrete.

Keywords: machine learning; compressive strength; concrete; rice husk ash (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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