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International Journal of Civil Engineering

, Volume 15, Issue 2, pp 213–221 | Cite as

ANN Model for Predicting the Compressive Strength of Circular Steel-Confined Concrete

  • M. Ahmadi
  • H. NaderpourEmail author
  • A. Kheyroddin
Research paper

Abstract

Concrete filled steel tube is constructed using various tube shapes to obtain most efficient properties of concrete core and steel tube. The compressive strength of concrete is considerably increased by the lateral confined steel tube in circular concrete filled steel tube (CCFT). The aim of this study is to present an integrated approach for predicting the steel-confined compressive strength of concrete in CCFT columns under axial loading based on large number of experimental data using artificial neural networks. Neural networks process information in a similar way that the human brain does. Neural networks learn by example. The main parameters investigated in this study include the compressive strength of unconfined concrete (\(f_{\text{c}}^{'}\)), outer diameter (D) and length (L) of column, wall thickness (t) and tensile yield stress (\(F_{\text{y}}\)) of steel tube. Subsequently, using the idealized network, empirical equations are developed for the confinement effect. The results of proposed model are compared with those of existing models on the basis of the experimental results. The findings indicate the precision and efficiency of ANN model for predicting the capacity of CCFT columns.

Keywords

Compressive strength Artificial neural network Confined concrete Column 

References

  1. 1.
    Ahmadi M, Naderpour H, Kheyroddin A (2014) Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load. Arch Civ Mech Eng 14:510–517CrossRefGoogle Scholar
  2. 2.
    Yu Z, Ding F, Cai CS (2007) Experimental behavior of circular concrete-filled steel tube stub columns. J Constr Steel Res 63:165–174CrossRefGoogle Scholar
  3. 3.
    Lai MH, Ho JCM (2015) Effect of continuous spirals on uni-axial strength and ductility of CFST columns. J Constr Steel Res 104:235–249CrossRefGoogle Scholar
  4. 4.
    Beheshti-Aval SB (2012) Strength evaluation of concrete-filled steel tubes subjected to axial-flexural loading by ACI and AISC-LRFD codes along with three dimensional nonlinear analysis. Int J Civ Eng 10:280–290Google Scholar
  5. 5.
    Perea T, Leon R (2014) Full-scale tests of slender concrete-filled tubes: interaction behavior. J Struct Eng 695:1–12Google Scholar
  6. 6.
    Rabunal JR, Dorado J (2006) Artificial neural networks in real-life applications. IGI GlobalGoogle Scholar
  7. 7.
    Ni H, Wang J (2000) Prediction of compressive strength of concrete by neural networks. Cem Concr Res 30:1245–1250CrossRefGoogle Scholar
  8. 8.
    Naderpour H, Kheyroddin A, Amiri GG (2010) Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Compos Struct 92:2817–2829CrossRefGoogle Scholar
  9. 9.
    Lee S-C (2003) Prediction of concrete strength using artificial neural networks. Eng Struct 25:849–857CrossRefGoogle Scholar
  10. 10.
    Oreta AWC, Kawashima K (2003) Neural network modeling of confined compressive strength and strain of circular concrete columns. J Struct Eng 129:554–561CrossRefGoogle Scholar
  11. 11.
    Kaveh A, Maniat M (2014) Damage detection in skeletal structures based on charged system search optimization using incomplete modal data. Int J Civil Eng Trans A Civil Eng 12(2):193–200Google Scholar
  12. 12.
    Effati M, Rajabi MA, Samadzadegan F, Shabani S (2014) A geospatial based neuro-fuzzy modeling for regional transportation corridors hazardous zones identification. Int J Civil Eng Trans A Civil Eng 12(3):289–303Google Scholar
  13. 13.
    Perera R, Barchín M, Arteaga A, De Diego A (2010) Prediction of the ultimate strength of reinforced concrete beams FRP-strengthened in shear using neural networks. Compos Part B Eng 41:287–298CrossRefGoogle Scholar
  14. 14.
    Kaveh A, Ghaffarian R (2015) Shape optimization of arch dams with frequency constraints by enhanced charged system search algorithm and neural network. Int J Civil Eng Trans A Civil Eng 13:102–111Google Scholar
  15. 15.
    Gardner NJ, Jacobson ER (1967) Structural behavior of concrete filled steel tubes. ACI J Proc 64:404–413Google Scholar
  16. 16.
    Gardner NJ (1968) Use of spiral welded steel tubes in pipe columns. ACI J Proc 65:937–942Google Scholar
  17. 17.
    Knowles RB, Park R (1969) Strength of concrete filled steel columns. J Struct Div 95:2565–2587Google Scholar
  18. 18.
    Cai SH (1984) A study on basic behavior and strength of concrete filled steel tubular short column. Structural Institute of China Building Research AcademyGoogle Scholar
  19. 19.
    Kitada T, Yoshida Y, Nakai H (1987) Fundamental study on elastoplastic behavior of concrete encased steel short tubular columns. Mem Fac Eng Osaka City Univ 28:237–253Google Scholar
  20. 20.
    Tomii M, Xiao Y, Sakino K (1988) Experimental study on the properties of concrete confined in circular steel tube. In: Proc. 2nd Int. Conf. Concrete Filled Steel Tubular Struct, Harbin, China, pp 24–30Google Scholar
  21. 21.
    Tsuji B, Nakashima M, Morita S (1991) Axial compression behavior of concrete filled circular steel tubes. In: Proc. 3rd Int. Conf. Steel-Concrete Compos. Struct, Fukuoka, Japan, pp 19–24Google Scholar
  22. 22.
    Luksha LK, Nesterovich AP (1991) Strength testing of large-diameter concrete filled steel tubular members. In: Wakabayashi M (ed) Proc. Third Intl. Conf. Steel-Concrete Compos. Struct, Sept, pp 67–72Google Scholar
  23. 23.
    Sakino K, Hayashi H (1991) Behavior of concrete filled steel tubular stub columns under concentric loading. In: Wakabayashi M (ed) Proc. Third Int. Conf. Steel-Concrete Compos. Struct., Sept., pp 25–30Google Scholar
  24. 24.
    O’Shea MD, Bridge RQ (2000) Design of circular thin-walled concrete filled steel tubes. J Struct Eng 126:1295–1303CrossRefGoogle Scholar
  25. 25.
    Kang HS, Lim SH, Moon TS (2002) Behavior of CFT stub columns filled with PCC on concentrically compressive load. J Arch Inst Korea 18:21–28Google Scholar
  26. 26.
    Giakoumelis G, Lam D (2004) Axial capacity of circular concrete-filled tube columns. J Constr Steel Res 60:1049–1068CrossRefGoogle Scholar
  27. 27.
    Han L-H, Yao G-H (2003) Behaviour of concrete-filled hollow structural steel (HSS) columns with pre-load on the steel tubes. J Constr Steel Res 59:1455–1475CrossRefGoogle Scholar
  28. 28.
    Han L-H, Yao G-H (2003) Influence of concrete compaction on the strength of concrete-filled steel RHS columns. J Constr Steel Res 59:751–767CrossRefGoogle Scholar
  29. 29.
    Sakino K, Nakahara H, Morino S, Nishiyama I (2004) Behavior of centrally loaded concrete-filled steel-tube short columns. J Struct Eng 130:180–188CrossRefGoogle Scholar
  30. 30.
    Zeghiche J, Chaoui K (2005) An experimental behaviour of concrete-filled steel tubular columns. J Constr Steel Res 61:53–66CrossRefGoogle Scholar
  31. 31.
    Oliveira WLA (2008) Theoretical–experimental analysis of circular concrete filled steel columns. Doctoral thesis. São Carlos School of Engineering, University of São PauloGoogle Scholar
  32. 32.
    Uy B, Tao Z, Han L-H (2011) Behaviour of short and slender concrete-filled stainless steel tubular columns. J Constr Steel Res 67:360–378CrossRefGoogle Scholar
  33. 33.
    Denavit MD, Hajjar JF (2010) Nonlinear seismic analysis of circular concrete-filled steel tube members and frames. Report No. NSEL-023, Newmark Structural Laboratory Report Series (ISSN 1940-9826). Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, UrbanaGoogle Scholar
  34. 34.
    Tully S (1997) A neural network approach for predicting the structural behaviour of concrete slabs. Master of engineering dissertation, Faculty of Engineering and Applied science memorial university of NewfoundlandGoogle Scholar
  35. 35.
    Hu YH, Hwang J-N (2001) Handbook of neural network signal processing. CRC pressGoogle Scholar
  36. 36.
    Beale MH, Hagan MT, Demuth HB (2010) Neural network toolbox 7: user’s guide. MathWorks, IncGoogle Scholar
  37. 37.
    Leung CK, Ng MY, Luk HC (2006) Empirical approach for determining ultimate FRP strain in FRP-strengthened concrete beams. J Compos Constr 10:125–138CrossRefGoogle Scholar
  38. 38.
    Shams M, Saadeghvaziri MA (1999) Nonlinear response of concrete-filled steel tubular columns under axial loading. ACI Struct J 96Google Scholar
  39. 39.
    Sen HK (1969) Triaxial effects in concrete-filled tubular steel columns. Imperial College London (University of London)Google Scholar
  40. 40.
    Hatzigeorgiou GD (2008) Numerical model for the behavior and capacity of circular CFT columns, Part II: Verification and extension. Eng Struct 30:1579–1589CrossRefGoogle Scholar

Copyright information

© Iran University of Science and Technology 2016

Authors and Affiliations

  1. 1.Faculty of Civil EngineeringSemnan UniversitySemnanIran

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