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May 11, 2026Scientific Reports0 citationsOpen Access

Evaluate the compressive strength of fiber-reinforced geopolymer concrete incorporating ground granulated blast furnace slag with machine learning approach

MSMd. Habibur Rahman SobuzINTI International University MKMd. Kawsarul Islam KabboKhulna University of Engineering and Technology SMSamir MazumderKhulna University of Engineering and Technology

Puntos clave

To evaluate the compressive strength of fiber-reinforced geopolymer concrete incorporating ground granulated blast furnace slag using a machine learning approach.
Utilized a machine learning framework to assess strength predictions.
Conducted a parametric analysis of various ratios influencing structural performance.
Employed data-driven techniques to optimize geopolymer mix design.
Achieved a prediction capability with a correlation coefficient of 0.79 and an RMSE of 9.74 MPa.
Identified water-solid ratio, activator Ca/Na ratio, and additive-binder ratio as key factors influencing strength variation.
Demonstrated a reduction in experimental efforts for geopolymer optimization.

Resumen

of about 0.79 and RMSE of 9.74 MPa, indicating reliable prediction capability for heterogeneous fiber-reinforced geopolymer systems. Parametric analysis showed that the water-solid ratio, activator Ca/Na ratio, and additive-binder ratio were the most influential parameters, contributing the majority of strength variation. The proposed framework offers a data-driven approach to reduce experimental effort while supporting sustainable geopolymer mix optimization.

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