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

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Md Alhaz UddinDepartment of Civil Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.

Md Habibur Rahman SobuzDepartment of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. habib@becm.kuet.ac.bd.

Md Kawsarul Islam KabboDepartment of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.

Ibrahim Y HakeemDepartment of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia. iyhakeem@nu.edu.sa.

Ratan LalDepartment of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

Ali MasriaDepartment of Civil Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.

Sani Aliyu AbubakarDepartment of Civil Engineering, Kampala International University, Western Campus, Ishaka -Bushenyi, Western Region, Kampala, Uganda. saliyu@kiu.ac.ug.

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6 · The paper itself

Abstract

The growing worldwide population has increased the use of electric arc furnaces (EAF), resulting in a surge of EAF slag and a huge environmental concern. EAF slag's complex physical qualities have a considerable impact on concrete's mechanical performance, mainly its compressive strength (CS). This study introduces a novel framework for forecasting the CS of EAF slag concrete that uses advanced machine learning models such as Extreme Gradient Boosting (XGB), AdaBoost (ADB), Random Forest (RF), Hybrid XGB-RF, and Hybrid XGB-ADB. A full dataset of 730 samples was meticulously created, containing essential input parameters such as binders, aggregates, and other necessary variables, with CS as the desired outcome. Based on the findings, the XGB model showed highest accuracy, with an test R

Indexed as

Arc furnace slag concreteCompressive strength predictionHybrid machine learning

Identifiers

PMID42069916

DOI10.1038/s41598-026-50606-y

PMCPMC13377197

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LicenceCC BY-NC-ND

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