Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

• Published in: Materials Vol. 15; no. 2; p. 647
• Main Authors: Shang, Meijun, Li, Hejun, Ahmad, Ayaz, Ahmad, Waqas, Ostrowski, Krzysztof Adam, Aslam, Fahid, Joyklad, Panuwat, Majka, Tomasz M.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.01.2022; MDPI
• Subjects: Accuracy; Aggregates; Algorithms; Classification; Compressive strength; Concrete; Construction materials; Correlation coefficients; Damage prevention; Data points; Decision trees; Environmental impact; Machine learning; Mechanical properties; Monte Carlo simulation; Natural resources; Recycled materials; Root-mean-square errors; Sensitivity analysis; Software; Statistical analysis; Superplasticizers; Tensile strength; Water absorption; compressive strength; concrete; fiber; mechanical properties; aggregate; split tensile strength
• DOI: 10.3390/ma15020647
• ISSN: 1996-1944

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.