Forecasting the rheological properties of alkali-activated concrete utilizing gene expression programming.

Hisham Alabduljabbar, Irfan Ullah, Muhammad Faisal Javed, Mohammed Jameel, Waseem Ullah Khan, Furqan Ahmad

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Article in Scientific reports, 2025. 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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6 authors.

Hisham AlabduljabbarDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia.

Irfan UllahCollege of Civil and Transportation Engineering, Hohai University, Nanjing, China. 19pwciv5205@uetpeshawar.edu.pk.

Muhammad Faisal JavedDepartment of Civil Engineering, GIK Institute of Engineering Sciences and Technology, Swabi, 23640, Pakistan.

Mohammed JameelDepartment of Civil Engineering, College of Engineering, King Saud University, 11362, Riyadh, Saudi Arabia.

Waseem Ullah KhanCollege of Computer Science and Software Engineering, Hohai University, Nanjing, China.

Furqan AhmadUNHCR, Kabul, Afghanistan. engr.furqanahmad123@gmail.com.

Funding

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6 · The paper itself

Abstract

Accurate prediction of fresh-state properties is essential for understanding the rheological behaviour of alkali-activated concrete (AAC), as these parameters directly influence mix uniformity and overall material performance. Gene expression programming (GEP) effectively captures complex relationships between mixture components and material properties, offering superior flexibility, interpretability, and adaptability compared to traditional regression and black-box models. It generates explicit mathematical equations suitable for engineering design while automatically selecting relevant features. Accordingly, this study employed gene expression programming (GEP) to develop a robust predictive model for the slump (SL), dynamic yield stress (DYS), static yield stress (SYS), and plastic viscosity (PV) of AAC, eliminating the need for time-consuming and resource-intensive physical experiments. The GEP model demonstrated excellent performance, achieving a coefficient of determination (R

Indexed as

AlkaliesConstruction MaterialsRheologyModels, TheoreticalViscosityAlkaliesAlkali-activated concreteGraphical user interfaceMachine learningRheological properties

Identifiers

PMID41436807

DOI10.1038/s41598-025-28892-9

PMCPMC12764896

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LicenceCC BY-NC-ND

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