Abstract
Detecting the impact of admixtures like fly ash and micro-silica on the mechanical property of concrete, especially the compressive strength (CS), earned a lot of attention not only in the concrete industry but also in future extended research and analysis. In this study, two innovative methods of hybrid support vector regression optimized by Arithmetic Optimization Algorithm and Antlion Optimization Algorithm named AOSVR and ALSVR were developed to generate accurately a trustable relationship between the feeding input (eight ingredients) of the model and the target values that optimizers by finding key variables of SVR lead to model precisely. These models applied to perform a prediction process of CS values for 170 High-Performance Concrete (HPC) samples. It can be concluded that the coefficient of determination values showed 0.9872 and 0.9850 for AOSVR and ALSVR, respectively. Moreover, the hybrid AOSVR model outperformed the most premier accuracy in the prediction of CS. Also, using these hybrid models helps diminish the cost of concrete testing and further the analysis of concrete mechanical characterization.
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Abbreviations
- CS:
-
Compressive strength
- HPC:
-
High-performance concrete
- B:
-
Binder content
- FA/B:
-
Fly ash to binder ratio
- MS/B:
-
Micro-silica to binder ratio
- CA/B:
-
Coarse aggregate to binder ratio
- CA/TA:
-
Coarse aggregate to total aggregate ratio
- W/B:
-
Water to binder ratio
- SP/B:
-
Superplasticizer to binder ratio
- CT:
-
Curing time
- RBFNN:
-
Radial base neural network
- AOA:
-
The arithmetic optimization algorithm
- ALO:
-
Antlion optimization algorithm
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Zhu, W., Huang, L. & Zhang, Z. Novel hybrid AOA and ALO optimized supervised machine learning approaches to predict the compressive strength of admixed concrete containing fly ash and micro-silica. Multiscale and Multidiscip. Model. Exp. and Des. 5, 391–402 (2022). https://doi.org/10.1007/s41939-022-00124-x
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DOI: https://doi.org/10.1007/s41939-022-00124-x