Materials and Structures

, Volume 49, Issue 10, pp 4319–4334 | Cite as

Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques

  • Iman Mansouri
  • Togay Ozbakkaloglu
  • Ozgur Kisi
  • Tianyu Xie
Original Article


This paper studies the ability of artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5Tree) techniques to predict ultimate conditions of fiber-reinforced polymer (FRP)-confined concrete. A large experimental test database that consists of over 1000 axial compression tests results of FRP-confined concrete specimens assembled from the published literature was used to train, test, and validate the models. The modeling results show that the ANN, ANFIS, MARS and M5Tree models fit well with the experimental test data. The M5Tree model performs better than the remaining models in predicting the hoop strain reduction factor and strength enhancement ratio, whereas the ANN model provided the most accurate estimates of the strain enhancement ratio. Performances of the proposed models are also compared with those of the existing conventional and evolutionary algorithm models, which indicate that the proposed ANN, ANFIS, MARS and M5Tree models exhibit improved accuracy over the existing models. The predictions of each proposed model are subsequently used to establish the interdependence of critical parameters and their influence on the behavior of FRP-confined concrete, which are discussed in the paper.


Neuro fuzzy Neural network Multivariate adaptive regression splines M5 model tree Fiber-reinforced polymer (FRP) Confined concrete 


  1. 1.
    Ozbakkaloglu T, Lim JC, Vincent T (2013) FRP-confined concrete in circular sections: review and assessment of stress–strain models. Eng Struct 49:1068–1088CrossRefGoogle Scholar
  2. 2.
    Richart FE, Brandtzaeg A, Brown RL (1928) A study of the failure of concrete under combined compressive stresses. Bulletin No. 185. Engineering Experimental Station. University of Illinois, ChampaignGoogle Scholar
  3. 3.
    Cevik A, Cabalar AF (2008) A genetic-programming-based formulation for the strength enhancement of fiber-reinforced-polymer-confined concrete cylinders. J Appl Polym Sci 110(5):3087–3095CrossRefGoogle Scholar
  4. 4.
    Cevik A, Guzelbey IH (2008) Neural network modeling of strength enhancement for CFRP confined concrete cylinders. Build Environ 43(5):751–763CrossRefGoogle Scholar
  5. 5.
    Cevik A, Gogus MT, Guzelbey IH, Filiz H (2010) Soft computing based formulation for strength enhancement of CFRP confined concrete cylinders. Adv Eng Softw 41(4):527–536CrossRefzbMATHGoogle Scholar
  6. 6.
    Naderpour H, Kheyroddin A, Amiri GG (2010) Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Compos Struct 92(12):2817–2829CrossRefGoogle Scholar
  7. 7.
    Cevik A (2011) Modeling strength enhancement of FRP confined concrete cylinders using soft computing. Expert Syst Appl 38(5):5662–5673MathSciNetCrossRefGoogle Scholar
  8. 8.
    Elsanadedy HM, Al-Salloum YA, Abbas H, Alsayed SH (2012) Prediction of strength parameters of FRP-confined concrete. Compos B 43(2):228–239CrossRefGoogle Scholar
  9. 9.
    Jalal M, Ramezanianpour AA (2012) Strength enhancement modeling of concrete cylinders confined with CFRP composites using artificial neural networks. Compos B 43(8):2990–3000CrossRefGoogle Scholar
  10. 10.
    Ozbakkaloglu T, Lim JC (2013) Axial compressive behavior of FRP-confined concrete: experimental test database and a new design-oriented model. Compos B 55:607–634CrossRefGoogle Scholar
  11. 11.
    Lim JC, Ozbakkaloglu T (2014) Confinement model for FRP-confined high-strength concrete. J Compos Constr ASCE 18(4):04013058CrossRefGoogle Scholar
  12. 12.
    Lee S, Lee C (2014) Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks. Eng Struct 61:99–112CrossRefGoogle Scholar
  13. 13.
    Sobhani J, Najimi M, Pourkhorshidi AR, Parhizkar T (2010) Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models. Constr Build Mater 24(5):709–718CrossRefGoogle Scholar
  14. 14.
    Güneyisi EM, Mermerdaş K, Güneyisi E, Gesoğlu M (2015) Numerical modeling of time to corrosion induced cover cracking in reinforced concrete using soft-computing based methods. Mater Struct 48(6):1739–1756CrossRefGoogle Scholar
  15. 15.
    Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–141MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Cheng M-Y, Cao M-T (2014) Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams. Eng Appl Artif Intell 28:86–96CrossRefGoogle Scholar
  17. 17.
    Quinlan JR (1992) Learning with continuous classes. Proc AI 92:343–348Google Scholar
  18. 18.
    Behnood A, Olek J, Glinicki MA (2015) Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm. Constr Build Mater 94:137–147CrossRefGoogle Scholar
  19. 19.
    Perera R, Ruiz A (2012) Design equations for reinforced concrete members strengthened in shear with external FRP reinforcement formulated in an evolutionary multi-objective framework. Compos B Eng 43(2):488–496CrossRefGoogle Scholar
  20. 20.
    Perera R, Tarazona D, Ruiz A, Martín A (2014) Application of artificial intelligence techniques to predict the performance of RC beams shear strengthened with NSM FRP rods. Formulation of design equations. Compos B Eng 66:162–173CrossRefGoogle Scholar
  21. 21.
    Kisi O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol Sci J 50(4):683–696Google Scholar
  22. 22.
    Haykin SS (2009) Neural networks and learning machines, 3rd edn. Prentice Hall, New JerseyGoogle Scholar
  23. 23.
    Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRefGoogle Scholar
  24. 24.
    Jang JSR, Sun CT (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River, New JerseyGoogle Scholar
  25. 25.
    Craven P, Wahba G (1979) Smoothing noisy data with spline functions. Numer Math 31(4):317–403MathSciNetzbMATHGoogle Scholar
  26. 26.
    Mitchell TM (1997) Machine learning. McGraw Hill, Burr Ridge, p 45zbMATHGoogle Scholar
  27. 27.
    Solomatine DP, Xue Y (2004) M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China. J Hydrol Eng 9(6):491–501CrossRefGoogle Scholar
  28. 28.
    Pal M, Deswal S (2009) M5 model tree based modelling of reference evapotranspiration. Hydrol Process 23(10):1437–1443CrossRefGoogle Scholar
  29. 29.
    Wang Y, Witten IH (1997) Induction of model trees for predicting continuous classes. In: Proceedings of the poster papers of the european conference on machine learning, 128–137Google Scholar
  30. 30.
    Lim JC, Ozbakkaloglu T (2014) Influence of silica fume on stress-strain behavior of FRP-confined HSC. Constr Build Mater 63:11–24CrossRefGoogle Scholar
  31. 31.
    Lim JC, Ozbakkaloglu T (2015) Investigation of the influence of application path of confining pressure: tests on actively confined and FRP-confined concretes. J Struct Eng ASCE 141(8):04014203CrossRefGoogle Scholar
  32. 32.
    May RJ, Maier HR, Dandy GC (2010) Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Netw 23:283–294CrossRefGoogle Scholar
  33. 33.
    Cigizoglu HK (2003) Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrol Sci J 48(3):349–361CrossRefGoogle Scholar
  34. 34.
    Pessiki S, Harries KA, Kestner JT, Sause R, Ricles JM (2001) Axial behavior of reinforced concrete columns confined with FRP jackets. J Compos Constr 5(4):237–245CrossRefGoogle Scholar
  35. 35.
    Lam L, Teng JG (2004) Ultimate condition of fiber reinforced polymer-confined concrete. J Compos Constr ASCE 8(6):539–548CrossRefGoogle Scholar
  36. 36.
    Lim JC, Ozbakkaloglu T (2015) Hoop strains in FRP-confined concrete columns: experimental observations. Mater Struct 48:2839–2854CrossRefGoogle Scholar
  37. 37.
    Rousakis TC, Karabinis AI, Kiousis PD, Tepfers R (2008) Analytical modelling of plastic behaviour of uniformly FRP confined concrete members. Compos B Eng 39(7):1104–1113CrossRefGoogle Scholar
  38. 38.
    Rousakis TC, Rakitzis TD, Karabinis AI (2012) Design-oriented strength model for FRP-confined concrete members. J Compos Constr 16(6):615–625CrossRefGoogle Scholar
  39. 39.
    Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993CrossRefGoogle Scholar
  40. 40.
    Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539CrossRefGoogle Scholar
  41. 41.
    Lam L, Teng JG (2003) Design-oriented stress-strain model for FRP-confined concrete. Constr Build Mater 17(6–7):471–489CrossRefGoogle Scholar
  42. 42.
    Bisby LA, Green MF, Kodur VKR (2005) Modeling the behavior of fiber reinforced polymer-confined concrete columns exposed to fire. J Compos Constr 9(1):15–24CrossRefGoogle Scholar
  43. 43.
    Jiang T, Teng JG (2007) Analysis-oriented stress-strain models for FRP-confined concrete. Eng Struct 29(11):2968–2986CrossRefGoogle Scholar
  44. 44.
    Teng JG, Huang YL, Lam L, Ye LP (2007) Theoretical model for fiber-reinforced polymer-confined concrete. J Compos Constr ASCE 11(2):201–210CrossRefGoogle Scholar
  45. 45.
    Al-Salloum Y, Siddiqui N (2009) Compressive strength prediction model for FRP-confined concrete. In: 9th international symposium on fiber reinforced polymer reinforcement for concrete structures, SydneyGoogle Scholar
  46. 46.
    Wu YF, Wang LM (2009) Unified strength model for square and circular concrete columns confined by external jacket. J Struct Eng 135(3):253–261CrossRefGoogle Scholar
  47. 47.
    Wu YF, Zhou YW (2010) Unified strength model based on Hoek-Brown failure criterion for circular and square concrete columns confined by FRP. J Compos Constr 14(2):175–184CrossRefGoogle Scholar
  48. 48.
    Realfonzo R, Napoli A (2011) Concrete confined by FRP systems: confinement efficiency and design strength models. Compos B 42(4):736–755CrossRefGoogle Scholar
  49. 49.
    Wei YY, Wu YF (2012) Unified stress-strain model of concrete for FRP-confined columns. Constr Build Mater 26(1):381–392CrossRefGoogle Scholar
  50. 50.
    Tamuzs V, Tepfers R, Zile E, Ladnova O (2006) Behavior of concrete cylinders confined by a carbon composite—3. Deformability and the ultimate axial strain. Mech Compos Mater 42(4):303–314CrossRefGoogle Scholar
  51. 51.
    Binici B (2005) An analytical model for stress-strain behavior of confined concrete. Eng Struct 27(7):1040–1051CrossRefGoogle Scholar
  52. 52.
    Youssef MN, Feng MQ, Mosallam AS (2007) Stress-strain model for concrete confined by FRP composites. Compos B 38(5–6):614–628CrossRefGoogle Scholar
  53. 53.
    Fahmy MFM, Wu ZS (2010) Evaluating and proposing models of circular concrete columns confined with different FRP composites. Compos B 41(3):199–213CrossRefGoogle Scholar
  54. 54.
    Jiang T, Teng JG (2006) Strengthening of short circular RC columns with FRP jackets: a design proposal. In: 3rd international conference on FRP composites in civil engineering, MiamiGoogle Scholar
  55. 55.
    Teng JG, Jiang T, Lam L, Luo Y (2009) Refinement of a design-oriented stress-strain model for FRP-confined concrete. J Compos Constr 13(4):269–278CrossRefGoogle Scholar
  56. 56.
    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):219–237CrossRefGoogle Scholar
  57. 57.
    Girgin ZC (2013) Modified johnston failure criterion from rock mechanics to predict the ultimate strength of fiber reinforced polymer (FRP) confined columns. Polymers 6(1):59–75CrossRefGoogle Scholar

Copyright information

© RILEM 2016

Authors and Affiliations

  • Iman Mansouri
    • 1
  • Togay Ozbakkaloglu
    • 2
  • Ozgur Kisi
    • 3
  • Tianyu Xie
    • 2
  1. 1.Department of Civil EngineeringBirjand University of TechnologyBirjandIran
  2. 2.School of Civil, Environmental and Mining EngineeringUniversity of AdelaideAdelaideAustralia
  3. 3.Department of Civil EngineeringCanik Basari UniversitySamsunTurkey

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