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Evaluating the bond strength of FRP in concrete samples using machine learning methods

  • Gao, Juncheng (China Vanke Co., Ltd.) ;
  • Koopialipoor, Mohammadreza (Faculty of Civil and Environmental Engineering, Amirkabir University of Technology) ;
  • Armaghani, Danial Jahed (Institute of Research and Development, Duy Tan University) ;
  • Ghabussi, Aria (Department of Civil Engineering, Central Tehran Branch, Islamic Azad University) ;
  • Baharom, Shahrizan (Department of Civil and Architectural Engineering, Eyvanekey University) ;
  • Morasaei, Armin (Department of Civil Engineeing, K.N. Toosi University of Technology) ;
  • Shariati, Ali (Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University) ;
  • Khorami, Majid (Facultad de Arquitectura y Urbanismo, Universidad UTE) ;
  • Zhou, Jian (School of Resources and Safety Engineering, Central South University)
  • Received : 2019.08.17
  • Accepted : 2020.03.05
  • Published : 2020.10.25
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Abstract

In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.

Keywords

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