Akademik Veri Yönetim Sistemi
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.169, ss.1-23, 2026 (SCI-Expanded, Scopus)
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.