Hybrid Machine learning-based modeling to predict and optimize the compressive strength of electric arc furnace slag-modified concrete.

Md Alhaz Uddin; Md Habibur Rahman Sobuz; Md Kawsarul Islam Kabbo; Ibrahim Y. Hakeem; Ratan Lal; Ali Masria; Sani Aliyu Abubakar

doi:10.1038/s41598-026-50606-y

May 2, 2026

Hybrid Machine learning-based modeling to predict and optimize the compressive strength of electric arc furnace slag-modified concrete.

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Key Points

The research aims to develop a machine learning model to predict and optimize the compressive strength of electric arc furnace slag-modified concrete.
Hybrid machine learning models were utilized to analyze compressive strength.
The influence of factors like curing period and cement content was evaluated using SHAP and partial dependence curves.
The study focuses on reducing reliance on traditional experimental methodologies.
The model achieved a performance value of 0.947 during the testing phase.
Curing period and cement content were identified as the most significant predictors of compressive strength.
The methodologies promote more efficient and ecologically conscious concrete design.

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Implication

Demonstrates predictive modeling of compressive strength in concrete, highlighting efficient design methods.

Abstract

value of 0.947 during the testing phase. Furthermore, explainability tools such as SHapley Additive ExPlanations (SHAP) and partial dependence curves (PDCs) identified the curing period and content of cement as the most influential factors to predict the CS of EAF slag concrete. The methodologies and outcomes of this study will help to reduce reliance on resource-intensive experimental methods, pave the way for efficient, precise, and ecologically conscientious concrete design.

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Uddin et al. (2026) studied this question.

synapsesocial.com/papers/69f9892215588823dae18068 https://doi.org/10.1038/s41598-026-50606-y

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Hybrid Machine learning-based modeling to predict and optimize the compressive strength of electric arc furnace slag-modified concrete.

Key Points

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Abstract

Discussion

Cite This Study

Hybrid Machine learning-based modeling to predict and optimize the compressive strength of electric arc furnace slag-modified concrete.

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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