Journal of Mechanical Science and Technology

, Volume 31, Issue 8, pp 3887–3895 | Cite as

Improved predictive model to the cross-sectional resistance of CFT

  • Iman Mansouri
  • Rolando Chacón
  • Jong Wan Hu


This paper proposes an improved theoretical prediction equation for Concrete-filled steel tubes (CFT) subjected to compressive forces. This ultimate load capacity is inferred from a database of 344 experimental results reported in the literature by using Gene expression programming (GEP). Moreover, a series of structural comparisons between design provisions, other mechanically-derived expressions and the proposed prediction are addressed. The levels of accuracy, practical use and phenomenological understanding of the phenomenon are pinpointed. The results obtained are in good agreement with both the experimental and theoretical predictions. Advantages and disadvantages of such type of predictions are pinpointed.


Concrete-filled tubes CFT Gene expression programming GEP 


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  1. [1]
    M. Johansson, The efficiency of passive confinement in CFT columns, Steel Compos. Struct., 2 (2002) 379–396.CrossRefGoogle Scholar
  2. [2]
    K. A. S. Susantha, H. Ge and T. Usami, A capacity prediction procedure for concrete-filled steel columns, J. Earthq. Eng., 5 (4) (2001) 483–520.CrossRefGoogle Scholar
  3. [3]
    G. D. Hatzigeorgiou, Numerical model for the behavior and capacity of circular CFT columns, Part I: Theory, Eng. Struct., 30 (6) (2008) 1573–1578.CrossRefGoogle Scholar
  4. [4]
    R. Chacón, E. Mirambell and E. Real, Resistance of concrete-filled tubular structures (CFT) on integral bridges, Inf. Constr., 64 (527) (2012) 355–370.CrossRefGoogle Scholar
  5. [5]
    Bradford Centre for Sustainable Environments (2014).Google Scholar
  6. [6]
    J. Hajjar, B. Gourley, C. Tort, M. Denavit and P. Schiller, Steel-concrete composite structural systems, Department of Civil and Environmental Engineering, Northeastern University (2013).Google Scholar
  7. [7]
    F. Zhou and W. Xu, Cyclic loading tests on concrete-filled double-skin (SHS outer and CHS inner) stainless steel tubular beam-columns, Eng. Struct., 127 (2016) 304–318.CrossRefGoogle Scholar
  8. [8]
    H. J. Kim, J. W. Hu and W. S. Hwang, Cyclic testing for structural detail improvement of CFT column-foundation connections, Sustain., 7 (5) (2015) 5260–5281.CrossRefGoogle Scholar
  9. [9]
    J. W. Hu and W. S. Hwang, Design and behavior of recentering beam-to-CFT column connections with super-elastic shape memory alloy fasteners, Int. J. Steel Struct., 13 (1) (2013) 55–69.CrossRefGoogle Scholar
  10. [10]
    J. W. Hu and R. T. Leon, Analyses and evaluations for composite-moment frames with SMA PR-CFT connections, Nonlin. Dyn., 65 (4) (2011) 433–455.CrossRefGoogle Scholar
  11. [11]
    J. W. Hu, E. Choi and R. T. Leon, Design, analysis and application of innovative composite PR connections between steel beams and CFT columns, Smart Mater. Struct., 20 (2) (2011).Google Scholar
  12. [12]
    J. W. Hu, J. Park and R. T. Leon, Advanced analysis and performance based evaluation of concrte filled tube (CFT) columns, Adv. Steel Constr., 6 (4) (2010) 1019–1033.Google Scholar
  13. [13]
    C. Ferreira, Gene expression programming: A new adaptive algorithm for solving problems, Complex Syst., 13 (2) (2001) 87–129.MathSciNetzbMATHGoogle Scholar
  14. [14]
    C. Ferreira, Gene expression programming: Mathematical Modeling by an Artificial Intelligence, 2 Ed., Springer, Germany (2006).zbMATHGoogle Scholar
  15. [15]
    C. Ferreira, Designing neural networks using gene expression programming, Advances in Soft Computing (2006) 517–535.Google Scholar
  16. [16]
    E. A. Colbourn, S. J. Roskilly, R. C. Rowe and P. York, Modelling formulations using gene expression programming-A comparative analysis with artificial neural networks, Eur. J. Pharm. Sci., 44 (3) (2011) 366–374.CrossRefGoogle Scholar
  17. [17]
    A. H. Gandomi, A. H. Alavi, S. Kazemi and M. Gandomi, Formulation of shear strength of slender RC beams using gene expression programming, part I: Without shear reinforcement, Autom. Constr., 42 (2014) 112–121.CrossRefGoogle Scholar
  18. [18]
    E. M. Güneyisi, M. D'Aniello, R. Landolfo and K. Mermerdas, A novel formulation of the flexural overstrength factor for steel beams, J. Constr. Steel Res., 90 (2013) 60–71.CrossRefGoogle Scholar
  19. [19]
    A. H. Gandomi, S. K. Babanajad, A. H. Alavi and Y. Farnam, Novel approach to strength modeling of concrete under triaxial compression, J. Mater. Civil Eng., 24 (9) (2012) 1132–1143.CrossRefGoogle Scholar
  20. [20]
    A. Gholampour, A. H. Gandomi and T. Ozbakkaloglu, New formulations for mechanical properties of recycled aggregate concrete using gene expression programming, Constr. Build. Mater., 130 (2017) 122–145.CrossRefGoogle Scholar
  21. [21]
    S. Jafari and S. S. Mahini, Lightweight concrete design using gene expression programing, Constr. Build. Mater., 139 (2017) 93–100.CrossRefGoogle Scholar
  22. [22]
    G. Abdollahzadeh, E. Jahani and Z. Kashir, Predicting of compressive strength of recycled aggregate concrete by genetic programming, Comput. Concrete., 18 (2) (2016) 155–164.CrossRefGoogle Scholar
  23. [23]
    A. H. Gandomi, A. H. Alavi, M. Gandomi and S. Kazemi, Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement, Measurement: J. Int. Measurement Confederation, 95 (2017) 367–376.CrossRefGoogle Scholar
  24. [24]
    M. E. M. Güneyisi, E. Güneyisi, M. E. Yilmaz and K. Mermerdas, Modeling and analysis of the shear capacity of adhesive anchors post-installed into uncracked concrete, Composites Part B: Eng., 60 (2014) 716–724.CrossRefGoogle Scholar
  25. [25]
    E. M. Güneyisi, M. Gesoğlu, E. Güneyisi and K. Mermerdas, Assessment of shear capacity of adhesive anchors for structures using neural network based model, Mater. Struct., 49 (3) (2016) 1065–1077.CrossRefGoogle Scholar
  26. [26]
    E. M. Güneyisi, A. Gültekin and K. Mermerdas, Ultimate capacity prediction of axially loaded CFST short columns, Int. J. Steel Struct., 16 (1) (2016) 99–114.CrossRefGoogle Scholar
  27. [27]
    I. Mansouri, J. W. Hu and O. Kisi, Novel predictive model of the debonding strength for masonry members retrofitted with FRP, Appl. Sci., 6 (11) (2016).Google Scholar
  28. [28]
    L. Chen, C. H. Kou and S. W. Ma, Prediction of slump flow of high-performance concrete via parallel hyper-cubic gene-expression programming, Eng. Appl. Artif. Intel., 34 (2014) 66–74.CrossRefGoogle Scholar
  29. [29]
    M. Saridemir, Empirical modeling of flexural and splitting tensile strengths of concrete containing fly ash by GEP, Computers and Concrete, 17 (4) (2016) 489–498.CrossRefGoogle Scholar
  30. [30]
    M. D'Aniello, E. M. Güneyisi, R. Landolfo and K. Mermerdas, Predictive models of the flexural overstrength factor for steel thin-walled circular hollow section beams, Thin-Wall. Struct., 94 (2015) 67–78.CrossRefGoogle Scholar
  31. [31]
    A. Johari, A. A. Javadi and H. Najafi, A genetic-based model to predict maximum lateral displacement of retaining wall in granular soil, Sci. Iranica, 23 (1) (2016) 54–65.CrossRefGoogle Scholar
  32. [32]
    E. Kanca, F. Çavdar and M. M. Ersen, Prediction of mechanical properties of cold rolled steel using genetic expression programming, Acta Phys. Pol. A, 130 (1) (2016) 365–369.CrossRefGoogle Scholar
  33. [33]
    I. F. Kara, Prediction of shear strength of FRP-reinforced concrete beams without stirrups based on genetic programming, Adv. Eng. Softw., 42 (6) (2011) 295–304.CrossRefzbMATHGoogle Scholar
  34. [34]
    Richart F., Brandtzaeg A. and Brown R., A study of the failure of concrete under combined compressive stresses, Bulletin. University of Illinois, 26 (12) (1928).Google Scholar
  35. [35]
    N. J. Gardner and E. R. Jacobson, Structural behavior of concrete filled steel tubes, ACI J., 64 (7) (1967) 404–413.Google Scholar
  36. [36]
    W. L. A. de Oliveira, S. De Nardin, A. L. H. de Cresce El Debs and M. K. El Debs, Influence of concrete strength and length/diameter on the axial capacity of CFT columns, J. Construct. Steel Res., 65 (12) (2009) 2103–2110.CrossRefGoogle Scholar
  37. [37]
    A. T. Beck, W. L. A. de Oliveira, S. De Nardim and A. L. H. C. ElDebs, Reliability-based evaluation of design code provisions for circular concrete-filled steel columns, Eng. Struct., 31 (10) (2009) 2299–2308.CrossRefGoogle Scholar
  38. [38]
    A. Kuranovas, D. Goode, A. K. Kvedaras and S. Zhong, Load-bearing capacity of concrete-filled steel columns, J. Civil Eng. Manage., 15 (1) (2009) 21–33.CrossRefGoogle Scholar
  39. [39]
    R. Chacón, E. Mirambell and E. Real, Strength and ductility of concrete-filled tubular piers of integral bridges, Eng. Struct., 46 (2013) 234–246.CrossRefGoogle Scholar
  40. [40]
    B. Evirgen, A. Tuncan and K. Taskin, Structural behavior of concrete filled steel tubular sections (CFT/CFSt) under axial compression, Thin-Wall. Struct., 80 (2014) 46–56.CrossRefGoogle Scholar
  41. [41]
    Z. H. Lu and Y. G. Zhao, Suggested empirical models for the axial capacity of circular CFT stub columns, J. Constr. Steel Res., 66 (6) (2010) 850–862.CrossRefGoogle Scholar
  42. [42]
    A. Mollahasani, A. H. Alavi and A. H. Gandomi, Empirical modeling of plate load test moduli of soil via gene expression programming, Computers and Geotechnics, 38 (2) (2011) 281–286.CrossRefGoogle Scholar
  43. [43]
    J. R. Koza, Genetic programming: On the programming of computers by means of natural selection, Cambridge: MIT Press (1992).zbMATHGoogle Scholar
  44. [44]
    Y. Pan, J. Jiang, R. Wang, H. Cao and Y. Cui, A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine, J. Hazard. Mater., 168 (2-3) (2009) 962–969.CrossRefGoogle Scholar
  45. [45]
    M. Ðurasević, D. Jakobović and K. Knežević, Adaptive scheduling on unrelated machines with genetic programming, Appl. Soft Comput., 48 (2016) 419–430.CrossRefGoogle Scholar
  46. [46]
    Design of composite steel and concrete structures. Part 1: General rules and rules for buildings, Design of Composite Steel and Concrete Structures (1994).Google Scholar
  47. [47]
    A. Aytek, O. Kisi and A. Guven, A genetic programming technique for lake level modeling, Hydrol. Res., 45 (4-5) (2014) 529–539.CrossRefGoogle Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Civil EngineeringBirjand University of TechnologyBirjandIran
  2. 2.Department of Civil and Environmental Engineering, School of Civil EngineeringTechnical University of CataloniaBarcelonaSpain
  3. 3.Department of Civil and Environmental EngineeringIncheon National UniversityIncheonKorea
  4. 4.Incheon Disaster Prevention Research CenterIncheon National UniversityYeonsu-gu, IncheonKorea

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