Skip to main content
Log in

Modeling the behavior of FRP-confined concrete using dynamic harmony search algorithm

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The accurate prediction of ultimate conditions for fiber reinforced polymer (FRP)-confined concrete is essential for the reliable structural analysis and design of resulting structural members. Nonlinear mathematical models can be used for accurate calibration of strength and strain enhancement ratios of FRP-confined concrete. In this paper, a new procedure is proposed to calibrate the nonlinear mathematical functions, which involved the use of a dynamic harmony search (DHS) algorithm. The harmony memory is dynamically adjusted based on a novel pitch generation scheme using a dynamic bandwidth and random number with normal standard distribution in DHS. A new design-oriented confinement model is proposed based on three influential factors of FRP area ratio (\( \rho_{a} \)), lateral confinement stiffness ratio (\( \rho_{E} \)), and strain ratio (\( \rho_{\varepsilon } \)). Five nonlinear mathematical design-oriented models are regressed on approximately 1000 axial compression tests of FRP-confined concrete in circular sections based on the proposed DHS algorithm. The proposed models for the prediction of the ultimate axial stress and strain of FRP-confined concrete are compared with the existing models. It has been shown that the DHS algorithm offers the best performance in terms of both accuracy and fast convergence rate in comparison with the other modified versions of harmony search algorithms for optimization problems. The proposed design-oriented model provides improved accuracy over the existing models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ozbakkaloglu T, Lim JC (2013) Axial compressive behavior of FRP-confined concrete: experimental test database and a new design-oriented model. Compos B 55:607–634

    Article  Google Scholar 

  2. Ozbakkaloglu T, Lim JC, Thomas V (2013) FRP-confined concrete in circular sections: review and assessment of stress–strain models. Eng Struct 49:1068–1088

    Article  Google Scholar 

  3. Sadeghian P, Fam A (2015) Improved design-oriented confinement models for FRP-wrapped concrete cylinders based on statistical analyses. Eng Struct 87:162–182

    Article  Google Scholar 

  4. Fardis MN, Khalili HH (1982) FRP-encased concrete as a structural material. Mag Concr Res 34(121):191–202

    Article  Google Scholar 

  5. Miyauchi K, Nishibayashi S, Inoue S (1997) Estimation of strengthening effects with carbon fiber sheet for concrete column. In: 3rd International symposium of non-metallic reinforcement for concrete structures

  6. Toutanji HA (1999) Stress–strain characteristics of concrete columns externally confined with advanced fiber composite sheets. ACI Mater J 96(3):397–404

    Google Scholar 

  7. Lam L, Teng JG (2003) Design-oriented stress–strain model for FRP-confined concrete. Constr Build Mater 17(6–7):471–489

    Article  Google Scholar 

  8. Teng JG, Jiang T, Lam L, Luo YZ (2009) Refinement of a design-oriented stress–strain model for FRP-confined concrete. J Compos Constr 13(4):269–278

    Article  Google Scholar 

  9. Rousakis T, Rakitzis T, Karabinis A (2012) Design-oriented strength model for FRP-confined concrete members. J Compos Constr 16(6):615–625

    Article  Google Scholar 

  10. Wu YF, Wei Y (2014) General stress–strain model for steel- and FRP-confined concrete. J Compos Constr. doi:10.1061/(ASCE)CC.1943-5614.0000511

    Google Scholar 

  11. Lim JC, Ozbakkaloglu T (2014) Confinement model for FRP-confined high-strength concrete. J Compos Constr 18(4):04013058. doi:10.1061/(ASCE)CC.1943-5614.0000376

    Article  Google Scholar 

  12. Pham TM, Hadi MNS (2014) Confinement model for FRP confined normal- and high-strength concrete circular columns. Constr Build Mater 69:83–90

    Article  Google Scholar 

  13. Hany NF, Hantouche EG, Harajli MH (2015) Axial stress–strain model of CFRP-confined concrete under monotonic and cyclic loading. J Compos Constr. doi:10.1061/(ASCE)CC.1943-5614.0000557

    Google Scholar 

  14. De Lorenzis L, Tepfers R (2003) Comparative study of models on confinement of concrete cylinders with fiber reinforced polymer composites. J Compos Constr 7(3):219–237

    Article  Google Scholar 

  15. Bisby LA, Dent AJS, Green MF (2005) Comparison of confinement models for fiber-reinforced polymer-wrapped concrete. ACI Struct J 102(1):62–72

    Google Scholar 

  16. Saadatmanesh H, Ehsani MR, Li MW (1994) Strength and ductility of concrete columns externally reinforced with fiber composite straps. ACI Struct J 91(4):434–447

    Google Scholar 

  17. Mander JB, Priestley MJN, Park R (1998) Theoretical stress–strain model for confined concrete. J Struct Eng 114(8):1804–1826

    Article  Google Scholar 

  18. Richart FE, Brandtzaeg A, Brown RL (1928) A study of the failure of concrete under combined compressive stresses. In: Bulletin no. 185, University of Illinois, Eng. Experimental Station: Champaign, Ill

  19. Ahmad SH, Shah SP (1982) Complete triaxial stress–strain curves for concrete. J Struct Div 108(4):728–742

    Google Scholar 

  20. Ahmad SM, Khaloo AR, Irshaid A (1991) Behaviour of concrete spirally confined by fiberglass filaments. Mag Concr Res 43(56):143–148

    Article  Google Scholar 

  21. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulations 76(2):60–68

    Article  Google Scholar 

  22. Wang X, Gao X, Zenger K (2015) An introduction to harmony search optimization method. Springer, Berlin

    Book  Google Scholar 

  23. Lim JC, Ozbakkaloglu T (2015) Influence of concrete age on stress–strain behavior of FRP-confined normal- and high-strength concrete. Constr Build Mater 82:61–70

    Article  Google Scholar 

  24. Lim JC, Ozbakkaloglu T (2014) Influence of silica fume on stress–strain behavior of FRP-confined HSC. Constr Build Mater 63:11–24

    Article  Google Scholar 

  25. Vincent T, Ozbakkaloglu T (2016) Influence of overlap configuration on compressive behavior of CFRP-confined normal- and high-strength concrete. Mater Struct 49(4):1245–1268. doi:10.1617/s11527-015-0574-x

    Article  Google Scholar 

  26. Xie T, Ozbakkaloglu T (2015) Behavior of steel fiber-reinforced high-strength concrete-filled FRP tube columns under axial compression. Eng Struct 90:158–171

    Article  Google Scholar 

  27. Lim JC, Ozbakkaloglu T (2014) stress–strain model for normal- and light-weight concretes under uniaxial and triaxial compression. Constr Build Mater 71:492–509

    Article  Google Scholar 

  28. Lim JC, Ozbakkaloglu T (2014) Unified stress–strain model for FRP and actively confined normal-strength and high-strength concrete. J Compos Constr. doi:10.1061/(ASCE)CC.1943-5614.0000536

    Google Scholar 

  29. Fahmy M, Wu Z (2010) Evaluating and proposing models of circular concrete columns confined with different FRP composites. Compos B 41(3):199–213

    Article  Google Scholar 

  30. Guralnick SA, Gunawan LM (2006) Strengthening of reinforced concrete bridge columns with FRP wrap. Pract Period Struct Des Constr 11(4):218–228

    Article  Google Scholar 

  31. Wu YF, Zhou Y (2010) Unified strength model based on Hoek–Brown failure criterion for circular and square concrete columns confined by FRP. J Compos Constr 14(2):175–184

    Article  Google Scholar 

  32. Newman K, Newman JB (1971) Failure theories and design criteria for plain concrete. In: Proc, international conference on structures, solid mechanics, and engineering design. New York City, NY: Wiley Interscience pp 936–995

  33. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579

    MathSciNet  MATH  Google Scholar 

  34. Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798

    Article  Google Scholar 

  35. Omran MGH, Mahdavi M (2008) Global-best harmony search. Appl Math Comput 198(2):643–656

    MathSciNet  MATH  Google Scholar 

  36. El-Abd M (2013) An improved global-best harmony search algorithm. Appl Math Comput 222:94–106

    MATH  Google Scholar 

  37. Keshtegar B, Oukati Sadeq M (2016) Gaussian global-best harmony search algorithm for optimization problems. Soft Comput 1–13. doi:10.1007/s00500-016-2274-z

  38. Pan Q, Suganthan PN, Liang JJ, Tasgetiren MF (2010) A local-best harmony search algorithm with dynamic subpopulations. Eng Optim 42(2):101–117

    Article  Google Scholar 

  39. Chen J, Pan Q, Wang L, Li J (2012) A hybrid dynamic harmony search algorithm for identical parallel machines scheduling. Eng Optim 44(2):209–224

    Article  MathSciNet  Google Scholar 

  40. Karthikeyan M, Sree Ranga Raja T (2015) Dynamic harmony search with polynomial mutation algorithm for valve-point economic load dispatch. Sci World J. doi:10.1155/2015/147678

    Google Scholar 

  41. Talarposhti K, Jamei M (2016) A secure image encryption method based on dynamic harmony search (DHS) combined with chaotic map. Opt Lasers Eng 81:21–34

    Article  Google Scholar 

  42. Willmott CJ (1981) On the validation of models. Phys Geogr 2(2):184–194

    Google Scholar 

  43. Keshtegar B, Piri J, Kisi O (2016) A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method. Comput Electron Agric 127:120–130

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Togay Ozbakkaloglu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Appendix

Appendix

DHS algorithm is defined based on the following steps to determine the unknown coefficients of the mathematical models. This algorithm can be implemented in a computer program to calibrate mathematical models with linear or nonlinear forms.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Keshtegar, B., Ozbakkaloglu, T. & Gholampour, A. Modeling the behavior of FRP-confined concrete using dynamic harmony search algorithm. Engineering with Computers 33, 415–430 (2017). https://doi.org/10.1007/s00366-016-0481-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-016-0481-y

Keywords

Navigation