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Neural Computing and Applications

, Volume 31, Issue 3, pp 711–717 | Cite as

Artificial neural network models for FRP-repaired concrete subjected to pre-damaged effects

  • Chau Khun Ma
  • Yeong Huei LeeEmail author
  • Abdullah Zawawi Awang
  • Wahid Omar
  • Shahrin Mohammad
  • Maybelle Liang
Original Article
  • 133 Downloads

Abstract

Confining damaged concrete columns using fibre-reinforced concrete (FRP) has proven to be effective in restoring strength and ductility. However, extensive experimental tests are generally required to fully understand the behaviour of such columns. This paper proposes the artificial neural networks (ANNs) models to simulate the FRP-repaired concrete subjected to pre-damaged loading. The models were developed based on two databases which contained the experimental results of 102 and 68 specimens for restored strength and strain, respectively. The proposed models agreed well with testing data with a general correlation factor of more than 97%. Subsequently, simplified equations in designing the restored strength and strain of FRP-repaired columns were proposed based on the trained ANN models. The proposed equations are simple but reasonably accurate and could be used directly in the design of such columns. The accuracy of the proposed equations is due to the incorporation of most affecting factors such as pre-damaged level, concrete compressive strength, confining pressure and ultimate confined concrete strength.

Keywords

Artificial neural network FRP concrete Pre-damaged effect Restored strength and strain 

Notes

Acknowledgements

This work was funded by Fundamental Research Grant Scheme (FRGS) from Ministry of Higher Education Malaysia (MOHE) with Grant No. 4F826. The supports from Universiti Teknologi Malaysia and MOHE are appreciated.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflicts of interest.

References

  1. 1.
    Wu YF, Yun Y, Wei Y, Zhou Y (2014) Effect of predamage on the stress–strain relationship of confined concrete under monotonic loading. J Struct Eng 140(12):04014093CrossRefGoogle Scholar
  2. 2.
    Li YF, Lin YJ, Chen CW, Lin CT (2007) Theoretical and experimental studies on repaired and rehabilitated reinforced concrete frames. Can J Civ Eng 34(8):923–933CrossRefGoogle Scholar
  3. 3.
    Iacobucci RD, Sheikh SA, Bayrak O (2003) Retrofit of square concrete columns with carbon fiber-reinforced polymer for seismic resistance. ACI Struct J 100(6):785–794Google Scholar
  4. 4.
    Li G, Hedlund S, Pang SS, Alaywan W, Eggers J, Abadie C (2003) Repair of damaged RC columns using fast curing FRP composites. Compos Part B Eng 34(3):261–271CrossRefGoogle Scholar
  5. 5.
    Bousias SN, Triantafillou TC, Fardis MN, Spathis L, O’Regan BA (2004) Fiber-reinforced polymer retrofitting of rectangular reinforced concrete columns with or without corrosion. Struct J 101(4):512–520Google Scholar
  6. 6.
    Gu DS, Wu G, Wu ZS, Wu YF (2010) Confinement effectiveness of FRP in retrofitting circular concrete columns under simulated seismic load. J Compos Constr 14(5):531–540CrossRefGoogle Scholar
  7. 7.
    Saadatmanesh H, Ehsani MR, Jin L (1997) Repair of earthquake-damaged RC columns with FRP wraps. ACI Struct J 94:206–215Google Scholar
  8. 8.
    Peled A (2007) Confinement of damaged and nondamaged structural concrete with FRP and TRC sleeves. J Compos Constr 11(5):514–522CrossRefGoogle Scholar
  9. 9.
    Tsonos AG (2008) Effectiveness of CFRP-jackets and RC-jackets in post-earthquake and pre-earthquake retrofitting of beam–column subassemblages. Eng Struct 30(3):777–793CrossRefGoogle Scholar
  10. 10.
    Wu X, Ghaboussi J, Garrett JH (1992) Use of neural networks in detection of structural damage. Comput Struct 42(4):649–659CrossRefzbMATHGoogle Scholar
  11. 11.
    Hadi MN (2003) Neural networks applications in concrete structures. Comput Struct 81(6):373–381CrossRefGoogle Scholar
  12. 12.
    Perera R, Arteaga A, De Diego A (2010) Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement. Compos Struct 92(5):1169–1175CrossRefGoogle Scholar
  13. 13.
    Hanna AS, Senouci AB (1995) NEUROSLAB–neural network system for horizontal formwork selection. Can J Civ Eng 22(4):785–792CrossRefGoogle Scholar
  14. 14.
    Chen KM, Tsai KK, Qi GZ, Yang ICS, Amuii F (1995) Ned network for structure control. J Comput Civ Eng ASCE 9(2):168–176CrossRefGoogle Scholar
  15. 15.
    Kang HT, Yoon CJ (1994) Neural network approaches to aid simple truss design problem. Microcomput Civ Eng 9:211–218CrossRefGoogle Scholar
  16. 16.
    Elkordy MF, Chang KC, Lee GC (1994) A structural damage neural network monitoring system. Microcomput Civ Eng 9:83–96CrossRefGoogle Scholar
  17. 17.
    Issa RRA, Fletcher D and Cade RA (1992) Predicting tower guy pretension using a neural network. In: Proceedings of the 8th conference of computing in civil engineering, ASCE, New York, pp 1074–1081Google Scholar
  18. 18.
    Chen SS, Shah K (1992) Neural networks in dynamic analysis of bridges. In: Proceedings of the 8th conference of computing in civil engineering, Dallas, Texas, pp 1058-–1065Google Scholar
  19. 19.
    Pratt D, Sansalone M (1992) Impact-echo signal interpretation using artificial intelligence. ACI Mater J 89(2):178–187Google Scholar
  20. 20.
    Moselhi O, Hegazy T, Fazio P (1992) Potential applications of neural networks in construction. Can J Civ Eng 19:521–529CrossRefGoogle Scholar
  21. 21.
    Hegay T (1993) Integrated bid preparation with emphases on risk assessrnent using neural networks. Ph.D. Thesis, Centre for Building Studies, Concordia University, Montreal, QuebecGoogle Scholar
  22. 22.
    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
  23. 23.
    Jalal M, Ramezanianpour AA (2012) Strength enhancement modeling of concrete cylinders confined with CFRP composites using artificial neural networks. Compos Part B Eng 43(8):2990–3000CrossRefGoogle Scholar
  24. 24.
    MATLAB R2012b. [Computer software]. The Math Works, Natick, MAGoogle Scholar
  25. 25.
    Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Net 2(5):359–366CrossRefzbMATHGoogle Scholar
  26. 26.
    Pham TM, Hadi MN (2014) Predicting stress and strain of FRP-confined square/rectangular columns using artificial neural networks. J Compos Constr 18(6):04014019CrossRefGoogle Scholar
  27. 27.
    Ma CK, Awang AZ, Omar W (2014) Slenderness effect and upper-bound slenderness limit of SSTT-confined HSC column. Int J Struct Eng 5(3):279–286CrossRefGoogle Scholar
  28. 28.
    Ma CK, Awang AZ, Omar W (2016) Flexural ductility design of confined high-strength concrete columns: theoretical modelling. Measurement 78:42–48CrossRefGoogle Scholar
  29. 29.
    Chau-Khun M, Awang AZ, Omar W, Pilakoutas K, Tahir MM, Garcia R (2015) Elastic design of slender high-strength RC circular columns confined with external tensioned steel straps. Adv Struct Eng 18(9):1487–1499CrossRefGoogle Scholar
  30. 30.
    Ma CK, Awang AZ, Garcia R, Omar W, Pilakoutas K, Azimi M (2016) Nominal curvature design of circular HSC columns confined with post-tensioned steel straps. Structures 7:25–32CrossRefGoogle Scholar
  31. 31.
    Ma CK, Awang AZ, Omar W (2014) New theoretical model of SSTT-confined HSC columns. Mag Concr Res 66(13):674–684CrossRefGoogle Scholar
  32. 32.
    Khun MC, Awang AZ, Omar W (2014) Slenderness limit for SSTT-confined HSC column. Struct Eng Mech 50(2):201–214CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Chau Khun Ma
    • 1
  • Yeong Huei Lee
    • 1
    Email author
  • Abdullah Zawawi Awang
    • 1
  • Wahid Omar
    • 1
  • Shahrin Mohammad
    • 1
  • Maybelle Liang
    • 2
  1. 1.Department of Structures and Materials, Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia
  2. 2.Department of Geotechnics and Transportation, Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia

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