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

• Published in: Mathematics (Basel) Vol. 12; no. 1; p. 66
• Main Authors: Kovačević, Miljan, Hadzima-Nyarko, Marijana, Grubeša, Ivanka Netinger, Radu, Dorin, Lozančić, Silva
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2024
• Subjects: Accuracy; Age; Algorithms; Artificial intelligence; Cement; Compressive strength; Concrete; Construction industry; Decision trees; Fly ash; Greenhouse effect; Investigations; Machine learning; Mean square errors; Methods; Neural networks; Portland cements; Regression analysis; Rice; rice husk ash; Sensitivity analysis; Temperature
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12010066

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.