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


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.


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



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.


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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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