Experimental investigation and explainable artificial intelligence-based modeling of punching shear behavior in self-compacting concrete flat-slabs with low hybrid fiber content

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dc.contributor.authorAri, Abdulkerim
dc.contributor.authorKatlav, Metin
dc.contributor.authorDonmez, Izzeddin
dc.contributor.authorTurk, Kazim
dc.date.accessioned2026-04-04T13:35:08Z
dc.date.available2026-04-04T13:35:08Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractFlat-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.
dc.description.sponsorshipInonu University Scientific Research Projects Committee [FYL-2023-3287]
dc.description.sponsorshipThe financial support for this study was granted by Inonu University Scientific Research Projects Committee (Project No: FYL-2023-3287) . We are thankful for their financial support.
dc.identifier.doi10.1016/j.engappai.2026.114116
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.orcid0000-0001-9093-7195
dc.identifier.orcid0000-0002-2721-4215
dc.identifier.scopus2-s2.0-105029539400
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2026.114116
dc.identifier.urihttps://hdl.handle.net/11616/109638
dc.identifier.volume169
dc.identifier.wosWOS:001688864400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofEngineering Applications of Artificial Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectPunching shear behavior
dc.subjectFlat-slab systems
dc.subjectLow fiber content
dc.subjectHybrid fiber reinforced self-compacting con-crete
dc.subjectMachine learning
dc.subjectArtificial intelligence-based modelling
dc.titleExperimental investigation and explainable artificial intelligence-based modeling of punching shear behavior in self-compacting concrete flat-slabs with low hybrid fiber content
dc.typeArticle

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