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Novel hybrid AOA and ALO optimized supervised machine learning approaches to predict the compressive strength of admixed concrete containing fly ash and micro-silica

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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Correspondence to Weixiang Zhu.

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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. (2022). https://doi.org/10.1007/s41939-022-00124-x

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  • DOI: https://doi.org/10.1007/s41939-022-00124-x

Keywords

  • High-performance concrete
  • Micro-silica
  • Arithmetic optimization algorithm
  • Antlion optimization algorithm