Öz
Determining the behavior of reinforced concrete (RC)
members is crucial in RC structures. The nonlinear attributes of RC members are
defined according to the cross sectional behavior of RC members to evaluate the
performance of structures. To be able to determine cross sectional behavior of
RC members, moment-curvature relationship should be known well. In the RC structures, using moment-curvature
(MC) relationship is the best way to represent cross sectional behavior and
nonlinear properties of RC members. The MC relationship of RC cross sections
can be evaluated by both experimentally or numerically. Some experimental
studies on RC members which are applied with 1:1 scale can be difficult to
define moment-curvature relationship. The purpose of the study is to obtain the
MC relationship of RC rectangular and circular
and circular columns numerically. By the way this study is tried to
achieve determining the parameters which affect MC relationship of RC members.
In the study, to evaluate MC relationship of RC members XTRACT programme which
represents influentially MC relationship is used. Compressive strength of
concrete, axial load on the RC sections, longitudinal and transverse
reinforcing ratio, are selected as comparison parameters which affect MC relationship.
As a consequence of this study curvature ductility and effective flexural
stiffness of RC rectangular and circular sections are determined using these
parameters. Effective flexural stiffness
is compared with the values defined in design codes. As a result of comparison,
it is observed that the moment curvature relationship can be defined as a
formulation according to the parameters which affect directly.
Anahtar Kelimeler
Kaynakça
- Adeli, H. and Samant, A. (2000), “An adaptive conjugate gradient neural network–wavelet model for traffic incident detection”, Comput. Aided Civil Infrastruct. Eng., 15 (4), 251–260. Arslan, M.H. (2012), “Estimation of curvature and displacement ductility in reinforced concrete buildings”, KSCE J. Civil Eng., 16 (5), 759-770. Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford University Press, Oxford, England. Caglar, N. and Garip, Z.S. (2013), “Neural network based model for seismic assessment of existing RC buildings”, Comput. Concr., 12, 229-242. Caglar, N. (2009), “Neural network based approach for determining the shear strength of circular reinforced concrete columns”, Constr. Build. Mater., 23, 3225-3232. Caglar N., Demir A., Ozturk H., Akkaya A., “A simple formulation for effective flexural stiffness of circular reinforced concrete columns”, Engineering Applications of Artificial Intelligence, 2015, 38(2015)79–87. Dogangun, A. (2013), Design and Calculation of Reinforced Concrete Structures, Birsen Press, Istanbul, Turkey, ISBN: 978-975-511-310-X. Ersoy, U. and Ozcebe, G. (1998), “Moment-curvature relationship of confined concrete sections”, IMO Technical Journal, December, Digest 98, 549-553. Ersoy, U., Ozcebe, G. and Tankut, T. (2008), Reinforced Concrete, Department of Civil Engineering, METU Press, Ankara, Turkey. Gunaratnam, D.J. and Gero, J.S. (2008), “Effect of representation on the performance of neural networks in structural engineering applications”, Comput. Aided Civil Infrastruct. Eng., 9(2), 97–108. Jadid, M.N. and Fairbairn D.R. (1996), “Neural-network applications in predicting moment-curvature parameters from experimental data”, Eng. Appl. Artif. Intell., 9 (3), 309-319. Mander, J.B., Priestley, M.J.N. and Park, R. (1988), “Theoretical stress-strain model for confined concrete”. J. Struct. Eng., 114(8), 1804-1826. Pala, M., Caglar, N., Elmas, M., Cevik, A. and Saribiyik, M. (2008), “Dynamic soil-structure interaction analysis of buildings with neural networks”, Constr. Build. Mater., 22(3), 330-342. Pala, M. (2006). “New formulation for distortional buckling stress”. J. Constr. Steel Res., 62, 716–722. Petschke, T., Corres, H.A., Ezeberry, J.I., Pérez, A. and Recupero, A. (2013), “Expanding the classic moment-curvature relation by a new perspective onto its axial strain”, Comput. Concr., An Int. J., 11 (6), 515-529. Kulkarni, A.D. (1994), “Artificial Neural Networks for Image Understanding”, Van Nosrand Reinhold, NY, USA TEC (2007), Turkish Earthquake Code, Ankara, Turkey. TS 500 (2002), Requirements for Design and Construction of Reinforced Concrete Structures, Ankara, Turkey. XTRACT and User Manual, “Cross-sectional X structural analysis of components, Imbsen Software Systems, 9912 Business Park Drive”, Suite 130 Sacramento, CA 95827.
Öz
Kaynakça
- Adeli, H. and Samant, A. (2000), “An adaptive conjugate gradient neural network–wavelet model for traffic incident detection”, Comput. Aided Civil Infrastruct. Eng., 15 (4), 251–260. Arslan, M.H. (2012), “Estimation of curvature and displacement ductility in reinforced concrete buildings”, KSCE J. Civil Eng., 16 (5), 759-770. Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford University Press, Oxford, England. Caglar, N. and Garip, Z.S. (2013), “Neural network based model for seismic assessment of existing RC buildings”, Comput. Concr., 12, 229-242. Caglar, N. (2009), “Neural network based approach for determining the shear strength of circular reinforced concrete columns”, Constr. Build. Mater., 23, 3225-3232. Caglar N., Demir A., Ozturk H., Akkaya A., “A simple formulation for effective flexural stiffness of circular reinforced concrete columns”, Engineering Applications of Artificial Intelligence, 2015, 38(2015)79–87. Dogangun, A. (2013), Design and Calculation of Reinforced Concrete Structures, Birsen Press, Istanbul, Turkey, ISBN: 978-975-511-310-X. Ersoy, U. and Ozcebe, G. (1998), “Moment-curvature relationship of confined concrete sections”, IMO Technical Journal, December, Digest 98, 549-553. Ersoy, U., Ozcebe, G. and Tankut, T. (2008), Reinforced Concrete, Department of Civil Engineering, METU Press, Ankara, Turkey. Gunaratnam, D.J. and Gero, J.S. (2008), “Effect of representation on the performance of neural networks in structural engineering applications”, Comput. Aided Civil Infrastruct. Eng., 9(2), 97–108. Jadid, M.N. and Fairbairn D.R. (1996), “Neural-network applications in predicting moment-curvature parameters from experimental data”, Eng. Appl. Artif. Intell., 9 (3), 309-319. Mander, J.B., Priestley, M.J.N. and Park, R. (1988), “Theoretical stress-strain model for confined concrete”. J. Struct. Eng., 114(8), 1804-1826. Pala, M., Caglar, N., Elmas, M., Cevik, A. and Saribiyik, M. (2008), “Dynamic soil-structure interaction analysis of buildings with neural networks”, Constr. Build. Mater., 22(3), 330-342. Pala, M. (2006). “New formulation for distortional buckling stress”. J. Constr. Steel Res., 62, 716–722. Petschke, T., Corres, H.A., Ezeberry, J.I., Pérez, A. and Recupero, A. (2013), “Expanding the classic moment-curvature relation by a new perspective onto its axial strain”, Comput. Concr., An Int. J., 11 (6), 515-529. Kulkarni, A.D. (1994), “Artificial Neural Networks for Image Understanding”, Van Nosrand Reinhold, NY, USA TEC (2007), Turkish Earthquake Code, Ankara, Turkey. TS 500 (2002), Requirements for Design and Construction of Reinforced Concrete Structures, Ankara, Turkey. XTRACT and User Manual, “Cross-sectional X structural analysis of components, Imbsen Software Systems, 9912 Business Park Drive”, Suite 130 Sacramento, CA 95827.
Ayrıntılar
| Konular | Mühendislik |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 9 Kasım 2017 |
| Yayımlandığı Sayı | Yıl 2017Sayı: 1 |
