Journal of Materials and Engineering Structures
Volume 12, Numéro 4, Pages 535-553
2025-12-30

Soft Computing Approaches For Tensile Strength Prediction Of Sustainable Concrete Mixtures



Authors : Rawa Aree Jamal . Taghreed Khaleefa Mohammed Ali . Halo Rebwar Nor . Sivand Sarkawt Saeed . Ahmed Salih Mohammed .

Abstract

Conventional concrete consumes large amounts of cement and aggregates, contributing to environmental degradation. This study investigates eco-friendly concrete mixtures incorporating industrial by-products such as fly ash, ground granulated blast furnace slag (GGBFS), and granite powder to enhance tensile strength and promote sustainable construction. A dataset of 252 experimental samples was analyzed using five statistical models—Linear Regression (LR), Pure Quadratic (PQ), Logarithmic (LOG), Interaction (INT), and Full Quadratic (FQ)—to predict tensile strength based on eight input parameters, including curing time, pull-off layer thickness, and mix ratios. Among the models, the FQ model exhibited the highest accuracy with the lowest residual error, followed by the INT model. Sensitivity analysis identified curing time, pull-off layer thickness, and the cement-to-sand ratio as key parameters affecting tensile strength. Results confirm that optimizing the water-to-cement and cement-to-sand ratios can significantly improve the performance of eco-friendly concrete. Overall, this study demonstrates that incorporating industrial by-products into concrete mixtures effectively balances mechanical performance with environmental sustainability, supporting the transition toward greener construction practices.

Keywords

Eco-friendly concrete ; Tensile Strength ; Soft Computing Models ; Supplemental Cementitious Materials

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