Browsing by Author "Yaşayanlar, Yonca"

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Citation - WoS: 2
Citation - Scopus: 1
Çelik Fiber Katkısının Farklı Boyuna Donatı Oranına Sahip Betonarme Döşemelerin Zımbalama Davranışı Üzerinde Etkileri
(Gazi Üniversitesi, 2019) Saatçi, Selçuk; Yaşayanlar, Süleyman; Yaşayanlar, Yonca; Batarlar, Baturay
In this study, reinforced concrete slabs in two groups, having 0.004 (D1 series) and 0.002 (D2 series) longitudinal reinforcement ratios in two orthogonal directions, were cast with concrete mixes containing 0%, 0.5%, 1% and 1.5% steel fiber ratios in volume. Slabs were 2150x2150x150 mm in dimensions. Eight slabs were tested in total under static loads. For slabs without steel fibers, the slab with higher reinforcement ratio showed punching failure before the yielding of longitudinal bars, whereas the slab with lower reinforcement ratio displayed a significantly higher ductility before final punching failure. Addition of steel fibers increased the punching load capacity up to two times. However, although addition of steel fibers also increased the maximum displacements in D1 series slabs, it did not make any significant effect on the maximum displacements of D2 series slabs. Maximum displacements were still controlled by the yielding of longitudinal reinforcement. Increasing the steel fiber ratio increased both the punching capacity and the maximum displacements in D1 series slabs, but it did not make a significant difference in behavior of D2 series beyond 1% fiber ratio. An analytical study of the test specimens were also performed using Critical Shear Crack Theory and based on comparisons of experimental and analytical results some improvements in the model were proposed. © 2019 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.
Efficiency of Shear Studs Manufactured From Threaded Bars on the Punching Behavior of Flat Slabs
(Golden Light Publishing, 2023) Saatçi, Selçuk; Saatcı, Selçuk; Yaşayanlar, Yonca
Punching resistance in flat slab systems in reinforced concrete structures is often provided with drop panels or shear reinforcement around columns. Shear studs are effectively used in these structures as shear reinforcement. However, factory-made shear studs may not be available in all locations and small quantities for small projects. Therefore, cheap shear studs that can be manufactured from widely available materials in small quantities can be very useful in certain cases. In this study, shear studs manufactured from threaded bars, widely available in hardware stores, are used for providing punching resistance to flat slabs. Stud heads were formed with T-section nuts. Four slab specimens, two with shear studs and two without, were cast and tested under concentrated loads at their mid-point. The slabs had 2150×2150×150 mm dimensions and they were cast with two different longitudinal reinforcement ratios. Test results showed that manufactured shear studs significantly increased the load and deformation capacities of the slabs. Slabs with shear studs were able to show up to three times higher bending deformations and they were able to sustain up to 50% higher loads. The study has shown that these studs can be effectively used for punching strengthening purposes in flat plate systems or in other cases where punching resistance is needed.
Material Model Calibration of Fiber Reinforced Concrete Using Deep Neural Network
(01. Izmir Institute of Technology, 2023) Yaşayanlar, Yonca; Saatcı, Selçuk; Erdem, Tahir Kemal; Saatcı, Selçuk; Erdem, Tahir Kemal
The numerical modeling of fiber reinforced concrete (FRC) structures is quite challenging due to the material's heterogeneous and anisotropic nature. The majority of material models that are suitable for regular concrete are not able to account for the FRC material's increased tensile capacity and ductility. In this study, a calibration method is proposed that is simple and effective for modeling FRC structures using a selected concrete material model. The Karagozian and Case (K&C) material model in LS-DYNA is capable of representing the ductile nature of FRC, and its parameters related to tensile behavior were calibrated to reflect the tensile-softening behavior. The calibration process was executed using the uniaxial direct tension test results of two different FRC mixtures. In addition, single element numerical models were constructed using LS-DYNA under uniaxial tension. The tensile parameters of K&C were varied over a wide range using single elements to form a database. Then, a Deep Neural Network (DNN) was constructed to pass the database through and find the K&C parameters that best matched the experimental uniaxial test results. The proposed methodology was tested under static and high-strain rate loading conditions, and the results were compared to the experimental findings. The performance of the DNN-achieved parameters was found to be satisfactory. The results showed that the DNN-calibrated parameters were able to accurately predict the behavior of FRC structures under static and dynamic loading conditions.