Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

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. 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):04014093

    Article  Google Scholar 

  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):923–933

    Article  Google Scholar 

  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):785–794

    Google Scholar 

  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):261–271

    Article  Google Scholar 

  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):512–520

    Google Scholar 

  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):531–540

    Article  Google Scholar 

  7. Saadatmanesh H, Ehsani MR, Jin L (1997) Repair of earthquake-damaged RC columns with FRP wraps. ACI Struct J 94:206–215

    Google Scholar 

  8. Peled A (2007) Confinement of damaged and nondamaged structural concrete with FRP and TRC sleeves. J Compos Constr 11(5):514–522

    Article  Google Scholar 

  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):777–793

    Article  Google Scholar 

  10. Wu X, Ghaboussi J, Garrett JH (1992) Use of neural networks in detection of structural damage. Comput Struct 42(4):649–659

    Article  MATH  Google Scholar 

  11. Hadi MN (2003) Neural networks applications in concrete structures. Comput Struct 81(6):373–381

    Article  Google Scholar 

  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):1169–1175

    Article  Google Scholar 

  13. Hanna AS, Senouci AB (1995) NEUROSLAB–neural network system for horizontal formwork selection. Can J Civ Eng 22(4):785–792

    Article  Google Scholar 

  14. Chen KM, Tsai KK, Qi GZ, Yang ICS, Amuii F (1995) Ned network for structure control. J Comput Civ Eng ASCE 9(2):168–176

    Article  Google Scholar 

  15. Kang HT, Yoon CJ (1994) Neural network approaches to aid simple truss design problem. Microcomput Civ Eng 9:211–218

    Article  Google Scholar 

  16. Elkordy MF, Chang KC, Lee GC (1994) A structural damage neural network monitoring system. Microcomput Civ Eng 9:83–96

    Article  Google Scholar 

  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 1074–1081

  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-–1065

  19. Pratt D, Sansalone M (1992) Impact-echo signal interpretation using artificial intelligence. ACI Mater J 89(2):178–187

    Google Scholar 

  20. Moselhi O, Hegazy T, Fazio P (1992) Potential applications of neural networks in construction. Can J Civ Eng 19:521–529

    Article  Google Scholar 

  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, Quebec

  22. Naderpour H, Kheyroddin A, Amiri GG (2010) Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Compos Struct 92(12):2817–2829

    Article  Google Scholar 

  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):2990–3000

    Article  Google Scholar 

  24. MATLAB R2012b. [Computer software]. The Math Works, Natick, MA

  25. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Net 2(5):359–366

    Article  MATH  Google Scholar 

  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):04014019

    Article  Google Scholar 

  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):279–286

    Article  Google Scholar 

  28. Ma CK, Awang AZ, Omar W (2016) Flexural ductility design of confined high-strength concrete columns: theoretical modelling. Measurement 78:42–48

    Article  Google Scholar 

  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):1487–1499

    Article  Google Scholar 

  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 7:25–32

    Article  Google Scholar 

  31. Ma CK, Awang AZ, Omar W (2014) New theoretical model of SSTT-confined HSC columns. Mag Concr Res 66(13):674–684

    Article  Google Scholar 

  32. Khun MC, Awang AZ, Omar W (2014) Slenderness limit for SSTT-confined HSC column. Struct Eng Mech 50(2):201–214

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Structures and Materials, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Chau Khun Ma, Yeong Huei Lee, Abdullah Zawawi Awang, Wahid Omar & Shahrin Mohammad

  2. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Maybelle Liang

Authors
  1. Chau Khun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yeong Huei Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdullah Zawawi Awang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wahid Omar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shahrin Mohammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maybelle Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yeong Huei Lee.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, C.K., Lee, Y.H., Awang, A.Z. et al. Artificial neural network models for FRP-repaired concrete subjected to pre-damaged effects. Neural Comput & Applic 31, 711–717 (2019). https://doi.org/10.1007/s00521-017-3104-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-3104-7

Keywords

Search

Navigation