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    Comprehensive experimental research on SCC with low hybrid fiber content: From workability to mechanical properties
    (Ernst & Sohn, 2026) Ari, Abdulkerim; Katlav, Metin; Turk, Kazim
    Hybrid fiber-reinforced self-compacting concrete (SCC) is gaining widespread popularity in construction engineering applications owing to its superior hardened concrete properties, while it isn't valid for workability, especially when it includes a high volume of fibers. While the incorporation of fibers can enhance the performance of SCC, it often challenges key fresh properties such as flowability and passing ability, thereby necessitating mix adjustments that result in additional costs. Furthermore, the high cost of fibers requires careful determination of the optimal fiber type combination and content. In this context, the aim and objective of this work is to comprehensively investigate the effects of different types (steel and synthetic) and combination (single, binary and ternary) of fibers on the workability (slump-flow, T500, J-ring and V-funnel) and mechanical (compressive strength, splitting tensile strength, elastic modulus, shear strength, and flexural tensile strength) properties of SCC mixes with low hybrid fiber content (total by volume 0.75%). After defining the workability properties of all mixes, mechanical property tests were performed on samples with curing periods of 7-, 28-, and 56-days. According to the results obtained, all fiber-reinforced SCC mixes exhibit high performance in terms of both workability and mechanical properties. In particular, binary hybrid fiber systems containing long hooked-end steel fibers and short straight steel fibers provided the best overall performance, yielding more effective outcomes compared to other mixes. In conclusion, this work has demonstrated that SCC mixes with low hybrid fiber content can successfully meet a wide range of engineering requirements, from workability to mechanical performance. This also implies that these mixes offer significant advantages in terms of both cost-effectiveness and ease of application due to workability superior of SCC mixes with low hybrid fiber content. These outcomes emphasize that SCC mixes with low hybrid fiber content can be safely used in structural concrete members subjected to complex loads such as punching, shear, and bending, and may offer more economical and sustainable alternatives to high-fiber content systems.
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    Experimental investigation and explainable artificial intelligence-based modeling of punching shear behavior in self-compacting concrete flat-slabs with low hybrid fiber content
    (Pergamon-Elsevier Science Ltd, 2026) Ari, Abdulkerim; Katlav, Metin; Donmez, Izzeddin; Turk, Kazim
    Flat-slab systems manufactured with self-compacting concrete (SCC) incorporating low hybrid fiber content offer a promising alternative for improving punching shear performance while enhancing constructability in building applications. In this paper, the punching shear behavior of flat-slabs produced with single, binary, and ternary fiber-reinforced SCC was experimentally investigated in terms of load-deflection response, ductility, toughness, cracking behavior, and failure mode. In parallel, a comprehensive database comprising 268 fiber-reinforced concrete flat-slab test results collected from the literature was established, and artificial intelligence (AI)based predictive models were developed to estimate punching shear capacity (Vpun). Model performance was evaluated using statistical indicators, whereas SHapley Additive exPlanations (SHAP) feature importance and partial dependence plots (PDPs) were employed to enhance interpretability and reveal the governing parameters influencing punching capacity. The outcomes demonstrate that binary hybrid fiber systems provide the most effective enhancement in punching capacity and post-cracking performance, even at low fiber contents, outperforming conventional solutions such as shear studs. Among the developed AI models, the Extra Trees Regressor and Random Forest algorithms exhibited the highest prediction accuracy for the Vpun. Finally, the AI models were integrated into a user-friendly graphical interface to facilitate practical engineering applications. Overall, this research contributes by experimentally validating low-fiber SCC flat-slabs as an efficient punching solution and by proposing an explainable, data-driven decision-support framework for engineering design.