Compressive strength optimization of natural zeolite-based geopolymers via taguchi design, grey relational analysis, and genetic algorithms

Abstract This study examines the optimization of natural zeolite-based ternary geopolymer mortars via an integrated Taguchi–Grey Relational Analysis–Genetic Algorithm (Taguchi–GRA–GA) framework to improve mechanical performance and durability. Natural zeolite (NZ) was partially substituted (up to 50 wt%) with fly ash (FA) and calcium hydroxide (CH) to enhance binder reactivity and matrix density. A Taguchi L9 orthogonal design was utilized to determine ideal activator values, subsequently employing GRA to amalgamate compressive strength findings at 7, 28, and 90 days into a singular performance index.The top-performing mixtures (F ₂₀ C ₂₀ , F ₂₅ C ₂₅ , and F ₃₀ C ₃₀ ) were experimentally validated and utilized to develop regression-based predictive models for subsequent GA optimization. The genetic algorithm identified an optimal formulation (NZ = 214.6 g dm ⁻³ , FA = 116.4 g dm ⁻³ , CH = 116.4 g dm ⁻³ ) that achieved a predicted compressive strength of 33.01 MPa, with experimental validation showing a deviation of less than 1.1%. This integrated method demonstrates that the combination of statistical design, data-driven modeling, and evolutionary optimization provides an efficient strategy for developing sustainable, high-performance binders. The resulting materials enhance strength and durability, allowing low-carbon, sustainable construction solutions aligned with global sustainability objectives.