PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | Vol. 14, no. 3 | 510--517
Tytuł artykułu

Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Due to high strength and ductility, concrete filled steel tube columns have been highly regarded in recent decades and many experimental studies have been carried out in predicting the strength of these columns. Increase in compressive strength of concrete core by the lateral confinement provided by steel tube and delay of the steel local buckling by the contact with the hardened concrete are effective parameters in behavior of concrete filled steel tubes. This study presents a new approach to predict the capacity of circular concrete filled steel tube columns under axial loading condition, using a large number of experimental data by applying artificial neural networks. The effects of yield stress and wall thickness of steel tube, compressive strength of concrete and dimensions of column are examined. Proposed equation is compared with other existing models and indicates that the new model can predict the ultimate strength of axially loaded columns by a high level of precision.
Wydawca

Rocznik
Strony
510--517
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
autor
  • Faculty of Civil Engineering, Semnan University, Semnan, Iran
  • Faculty of Civil Engineering, Semnan University, Semnan, Iran
Bibliografia
  • [1] S. Tokgoz, C. Dundar, Experimental study on steel tubular column in-filled with plain and steel fiber reinforced concrete, Thin-Walled Structures 48 (6) (2010) 414–422.
  • [2] N.J. Gardner, E.R. Jacobson, Structural behavior of concrete filled steel tubes, Journal of American Concrete Institute 64 (7) (1967) 404–413.
  • [3] T. Kitada, Y. Yoshida, H. Nakai, Fundamental Study on Elastoplastic Behavior of Concrete Encased Steel Short Tubular Columns, vol. 28, Memoirs of the Faculty of Engineering, Osaka City University, Osaka, Japan, 1987pp. 237–253.
  • [4] M.D. O'Shea, R.Q. Bridge, Design of circular thin-walled concrete-filled steel tubes, Journal of Structural Engineering-ASCE 126 (11) (2000) 1295–1303.
  • [5] G. Giakoumelis, D. Lam, Axial capacity of circular concrete- filled tube columns, Constructional Steel Research 60 (7) (2004) 1049–1068.
  • [6] K. Sakino, H. Nakahara, S. Morino, I. Nishiyama, Behavior of centrally loaded concrete-filled steel-tube short columns, Journal of Structural Engineering-ASCE 130 (2) (2004) 180–188.
  • [7] B. Uy, Z. Tao, L.H. Han, Behavior of short and slender concrete- filled stainless steel tubular columns, Constructional Steel Research 67 (3) (2011) 360–378.
  • [8] N.J. Gardner, Use of spiral welded steel tubes in pipe columns, Journal of American Concrete Institute 65 (11) (1968) 937–942.
  • [9] R.B. Knowles, R. Park, Strength of concrete filled steel tubular columns, Journal of Structural Division-ASCE 95 (12) (1969) 2565–2587.
  • [10] S.H. Cai, A Study on Basic Behavior and Strength of Concrete Filled Steel Tubular Short Column, Structural Institute of China Building Research Academy, 1984.
  • [11] M. Tomii, Y. Xiao, K. Sakino, Experimental study on the properties of concrete confined in circular steel tube, in: Proceedings of the International Specialty Conference on Concrete Filled Steel Tubular Structures, Harbin, China, 1988.
  • [12] B. Tsuji, M. Nakashima, S. Morita, Axial compression behavior of concrete filled circular steel tubes, in: M. Wakabayashi (Ed.), Proceedings of the Third International Conference on Steel–Concrete Composite Structures, Fukuoka, Japan, Association for International Cooperation and Research in Steel–Concrete Composite Structures, 1991.
  • [13] K. Sakino, H. Hayashi, Behavior of concrete filled steel tubular stub columns under concentric loading, in: M. Wakabayashi (Ed.), Proceedings of the Third International Conference on Steel–Concrete Composite Structures, Fukuoka, Japan, Association for International Cooperation and Research in Steel–Concrete Composite Structures, 1991.
  • [14] L.K. Luksha, A.P. Nesterovich, Strength testing of large-diameter concrete filled steel tubular members, in: M. Wakabayashi (Ed.), Proceedings of the Third International Conference on Steel–Concrete Composite Structures, Fukuoka, Japan, Association for International Cooperation and Research in Steel–Concrete Composite Structures, 1991.
  • [15] H.S. Kang, S.H. Lim, T.S. Moon, Behavior of CFT stub columns filled with PCC on concentrically compressive load, Journal of the Architectural Institute of Korea 18 (9) (2002) 21–28.
  • [16] L.H. Han, G.H. Yao, Influence of concrete compaction on the strength of concrete-filled steel RHS columns, Constructional Steel Research 59 (6) (2003) 751–767.
  • [17] L.H. Han, G.H. Yao, Behavior of concrete-filled hollow structural steel (HSS) columns with pre-load on the steel tubes, Constructional Steel Research 59 (12) (2003) 1455–1475.
  • [18] W.L.A Oliveira, Theoretical–experimental analysis of circular concrete filled steel columns, Doctoral thesis, Sao Carlos School of Engineering, University of Sao Paulo, 2008.
  • [19] J. Zeghiche, K. Chaoui, An experimental behavior of concrete- filled steel tubular columns, Constructional Steel Research 61 (1) (2005) 53–66.
  • [20] W.S. McCulloch, W.H. Pitts, A logical calculus of ideas immanent in nervous activity, Bulletin of Mathematical Biophysics 5 (1943) 115–133.
  • [21] P. Van Der Smagt, B. Krose, An Introduction to Neural Networks, eighth ed., University of Amsterdam, Netherland, 1996.
  • [22] H. Demuth, M. Beale, M. Hagan, Neural Network Toolbox 6: User's Guide Version 6.0.2, MathWorks, Inc., 2009.
  • [23] Y.H. Hu, J. Hwang, Handbook of Neural Network Signal Processing, CRC Press, USA, 2001.
  • [24] A.W.C Oreta, K. Kawashima, Neural network modeling of confined compressive strength and strain of circular concrete columns, Journal of Structural Engineering-ASCE 129 (4) (2003) 554–561.
  • [25] C.W. Tang, H.J. Chen, T. Yen, Modeling confinement efficiency of reinforced concrete columns with rectilinear transverse steel using artificial neural networks, Journal of Structural Engineering-ASCE 129 (6) (2003) 775–783.
  • [26] H. Naderpour, A. Kheyroddin, G. Ghodrati Amiri, Prediction of FRP-confined compressive strength of concrete using artificial neural networks, Composite Structures 92 (12) (2010) 2817–2829.
  • [27] M. Uysal, H. Tanyildizi, Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network, Construction and Building Materials 25 (11) (2011) 4105–4111.
  • [28] H.M. Tanarslan, M. Secer, A. Kumanlioglu, An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks, Construction and Building Materials 30 (2012) 556–568.
  • [29] A.T.A. Dantas, M.B. Leite, K.J. Nagahama, Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks, Construction and Building Materials 38 (2013) 717–722.
  • [30] M. Shams, M.A. Saadeghvaziri, Nonlinear response of concrete-filled steel tubular columns under axial loading, ACI Structural Journal 96 (6) (1999) 1009–1017.
  • [31] H.K. Sen, Triaxial effects in concrete-filled tubular steel columns, (Ph.D. thesis), University of London, England, 1969.
  • [32] G.D. Hatzigeorgiou, Numerical model for the behavior and capacity of circular CFT columns. Part II: Verification and extension, Engineering Structures 30 (6) (2008) 1579–1589.
  • [33] C.K.Y Leung, M.Y. Ng, H.C. Luk, Empirical approach for determining ultimate FRP strain in FRP-strengthened concrete beams, Journal of Composites Construction-ASCE 10 (2) (2006) 125–138.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-53cf355d-8c03-4316-affe-9c1a7c3aefd5
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.