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September 10, 2025REVIEWS ON ADVANCED MATERIALS SCIENCEOpen Access

Advanced explainable models for strength evaluation of self-compacting concrete modified with supplementary glass and marble powders

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Authors

KKKaffayatullah KhanKing Faisal University MKMohammad Muzzaffar KhanCOMSATS University Islamabad AAAhmed A. Alawi Al-NaghiIllinois College

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Implication

This analysis uses machine learning techniques to predict compressive strength in self-compacting concrete, indicating a new approach to sustainable construction.

Key Points

Random forest model achieved highest accuracy in predicting compressive strength of self-compacting concrete.
Machine learning techniques efficiently analyzed datasets with a notable R² value of 0.95 for the best model.
Curing time was identified as the most influential feature through advanced interpretability methods like Shapley Additive ExPlanations.
Research promotes sustainable construction practices by utilizing waste materials like glass and marble powders in concrete.

Cite This Study

Khan et al. (2025) studied this question.

synapsesocial.com/papers/68c1ae7054b1d3bfb60e653d https://doi.org/10.1515/rams-2025-0128

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Implication

This analysis uses machine learning techniques to predict compressive strength in self-compacting concrete, indicating a new approach to sustainable construction.

Advanced explainable models for strength evaluation of self-compacting concrete modified with supplementary glass and marble powders

Discussion

Member takes

Implication

Key Points

Cite This Study

Advanced explainable models for strength evaluation of self-compacting concrete modified with supplementary glass and marble powders

Discussion

Member takes

Implication

Key Points

Cite This Study

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