Browsing by Author "Turk, Furkan"

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    3D Printable Mortars with Green Materials: Sustainable Solutions with Nanocellulose
    (Taylor & Francis Ltd, 2025) Turk, Ayse; Turk, Furkan; Edebali, Serpil; Keskin, Ulku Sultan
    This study aims to improve the fresh and hardened properties of 3D-printable mortars. For this purpose, mortar mixtures containing cellulose nanofibres (CNF) obtained from the green algae of the Cladophora sp., which is abundant in lakes and causes environmental pollution if not recycled, as well as commercially available cellulose nanocrystals (CNC) and welan gum (WG) were prepared. The results showed that CNF was the most effective additive in improving fresh-state rheology, increasing yield stress by approximately 6 times and thixotropy by 48 times compared to the reference sample. Additionally, the CNF-modified mortar could carry a load of 50 N in the fresh state. The CNC additive showed the best mechanical performance, increasing compressive strength by 12%. Cellulosic additives were also observed to reduce shrinkage. Nanocellulose additives also increased the interlayer adhesion strength. All additives used have improved the properties of 3D-printable mortars. This study successfully produced a 3D-printable concrete/mortar additive from Cladophora sp., an environmentally detrimental waste material.
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    Compressive Strength Optimization of Natural Zeolite-Based Geopolymers Via Taguchi Design, Grey Relational Analysis, and Genetic Algorithms
    (IOP Publishing Ltd, 2025) Liban, Roble Ibrahim; Turk, Furkan; Keskin, Ulku Sultan; Yildizel, Sadik Alper
    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 (F20C20, F25C25, and F30C30) 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.