Structural Engineering and Mechanics

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Data-driven prediction of compressive strength of FRP-confined concrete members: An application of machine learning models

  • Berradia, Mohammed (Department of Civil Engineering, Laboratory of Structures, Geotechnics and Risks (LSGR), Hassiba Benbouali University of Chlef) ;
  • Azab, Marc (College of Engineering and Technology, American University of the Middle East) ;
  • Ahmad, Zeeshan (Department of Civil Engineering, University of Engineering and Technology) ;
  • Accouche, Oussama (College of Engineering and Technology, American University of the Middle East) ;
  • Raza, Ali (Department of Civil Engineering, Laboratory of Structures, Geotechnics and Risks (LSGR), Hassiba Benbouali University of Chlef) ;
  • Alashker, Yasser (Civil Engineering Department, College of Engineering, King Khalid University)
  • Received : 2022.01.30
  • Accepted : 2022.06.11
  • Published : 2022.08.25
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Abstract

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R2) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R2=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

Keywords

Acknowledgement

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under grant number R.G.P.1/221/43.

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