Affiliations: [a]
School of Civil and Architectural Engineering, Anyang Institute of Technology, Anyang, Henan, China
| [b]
Hebei University of Technology, Tangshan, Hebei, China
Correspondence:
[*]
Corresponding author. Libo Wang, School of Civil and Architectural Engineering, Anyang Institute of Technology, Anyang, Henan, 455000, China. E-mail: [email protected].
Abstract: Concrete is known as one of the most important materials in the world. Concrete composites consisting of cement, water, aggregates, and some additives are used to improve the properties of concrete. These concrete have a certain compressive strength that can be increased depending on the type of concrete. In line with these ideas, high-performance concrete (HPC) has been produced, which can have high compressive strength by adding materials such as fly ash, silica fume, etc. This type of concrete is used in bridges, dams, and special constructions. However, obtaining the mixture design of HPC is problematic and complex, for this reason, the machine learning methods can make it easy to achieve the output by saving time and energy. This study has used support vector regression (SVR) to predict the compressive strength of HPC. Moreover, this study provided two meta-heuristic algorithms for obtaining suitable and optimized results, which are contained the artificial hummingbird algorithm (AHA) and Sine Cosine Algorithm (SCA). The model by coupling with algorithms created the hybrid method in the framework of SVR-AHA and SVR-SCA. Furthermore, some criteria indicators have been used for determining the most desirable hybrid model, which is included coefficient of correlation (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and weight absolute percentage error (WAPE). As a result, the AHA algorithm could have a more satisfying association model with the SVR model, and the results were RMSE = 2.00 (MPa), R2 = 98.59%, MAE = 0.717 (MPa), MAPE = 1.22 (MPa), and WAPE = 0.114 (MPa).
Keywords: High-performance concrete, sine cosine algorithm, artificial hummingbird algorithm, support vector regression, compressive strength
