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

Materials

Tomasz M. Majka¹  Ayaz Ahmad²  Waqas Ahmad²  Fahid Aslam³  Panuwat Joyklad⁴  Krzysztof Adam Ostrowski⁵  Hejun Li⁶  Meijun Shang⁷ 

[1] Department of Chemistry and Technology of Polymers, Faculty of Chemical Engineering and Technology, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland;Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan;Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;Department of Civil and Environmental Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhonnayok 26120, Thailand;Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland;Jilin Northeast Architectural and Municipal Engineering Design Institute Co., Ltd., Changchun 130062, China;School of Architetrue and Civil Engineering, Changchun Sci-Tech Unversity, Changchun 130600, China;

关键词: mechanical properties;    aggregate;    concrete;    compressive strength;    split tensile strength;    fiber;   

DOI  :  10.3390/ma15020647

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown