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This investigation aims to optimize the compressive strength of alkali-activated slag concrete using machine learning approaches.

May 9, 2026Scientific ReportsOpen Access

Microstructural and mechanical characteristics evaluation of alkali-activated slag concrete and optimize with supervised hybrid machine learning modeling

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Randomized trial evaluates compressive strength in alkali-activated concrete, suggesting optimal mixtures for better performance.

This investigation aims to optimize the compressive strength of alkali-activated slag concrete using machine learning approaches.
Utilized four supervised machine learning models to estimate compressive strength of Fly ash-based alkali-activated concrete.
Produced 650 samples with varying slag proportions and curing at 25 °C and 90 °C.
Employed performance indicators like MAPE, RMSE, and R2 to assess model accuracy.
The hybrid XGB-DT model achieved the highest precision with R2 = 0.986 and RMSE = 2.44 MPa.
Higher slag content improved compressive strength due to denser microstructure and improved C-S-H linkages.
SHAP and PDP analyses provided optimal ranges for parameters affecting strength.

Kabbo et al. (2026) studied this question.

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May 9, 2026Scientific ReportsOpen Access

View Full Paper

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Authors

Randomized trial evaluates compressive strength in alkali-activated concrete, suggesting optimal mixtures for better performance.

This investigation aims to optimize the compressive strength of alkali-activated slag concrete using machine learning approaches.
Utilized four supervised machine learning models to estimate compressive strength of Fly ash-based alkali-activated concrete.
Produced 650 samples with varying slag proportions and curing at 25 °C and 90 °C.
Employed performance indicators like MAPE, RMSE, and R2 to assess model accuracy.
The hybrid XGB-DT model achieved the highest precision with R2 = 0.986 and RMSE = 2.44 MPa.
Higher slag content improved compressive strength due to denser microstructure and improved C-S-H linkages.
SHAP and PDP analyses provided optimal ranges for parameters affecting strength.

Kabbo et al. (2026) studied this question.

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