Radial Basis Function Coupling with Metaheuristic Algorithms for Estimating the Compressive Strength and Slump of High-Performance Concrete
Compressive strength (CS) and slump flow (SL) are two of the most essential parameters in High-Performance Concrete (HPC), which directly affect its structural capacity, durability, and workability. The following study represents an important step toward developing novel hybrid models for predicting...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Bilijipub publisher
2024-12-01
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| Series: | Advances in Engineering and Intelligence Systems |
| Subjects: | |
| Online Access: | https://aeis.bilijipub.com/article_212435_1af531df4021b0e09cf936b09776bdd9.pdf |
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| Summary: | Compressive strength (CS) and slump flow (SL) are two of the most essential parameters in High-Performance Concrete (HPC), which directly affect its structural capacity, durability, and workability. The following study represents an important step toward developing novel hybrid models for predicting CS and SL. The contribution in this paper proposes the following: the radial basis function (RBF) model will be enhanced by using two optimization algorithms, namely Horse Herd Optimization (HHO) and Wild Geese Algorithm (WGA). Accordingly, two hybrid models have been proposed, referred to as RBFH and RBWG. For model testing, comprehensive metrics include the coefficient of determination (R²), RMSE, MAE, VAF, and SI. It has the highest R² values of 98.17 for CS and 93.54 for SL predictions among all the datasets, while it also records the lowest SI values of 0.064 and 0.037 for CS and SL, respectively. These are indicative of the accuracy and reliability of the RBWG model in modelling the properties of HPC. This work's significance consists of improving the concrete mix design by giving correct predictions of HPC performance, which will lead to optimized resource utilization, minimized costs, and reduced negative environmental impacts due to construction. The results highlight hybrid machine learning models as the potential to solve complex challenges in civil engineering and provide new approaches toward sustainable and efficient infrastructure development. |
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| ISSN: | 2821-0263 |