Computers and Concrete

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Identification of shear transfer mechanisms in RC beams by using machine-learning technique

  • Zhang, Wei (Department of Architectural Engineering, Chungbuk National University) ;
  • Lee, Deuckhang (Department of Architectural Engineering, Chungbuk National University) ;
  • Ju, Hyunjin (School of Architecture and Design Convergence, Hankyong National University) ;
  • Wang, Lei (School of Civil Engineering, Changsha University of Science & Technology)
  • Received : 2022.02.09
  • Accepted : 2022.06.15
  • Published : 2022.07.25
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Abstract

Machine learning technique is recently opening new opportunities to identify the complex shear transfer mechanisms of reinforced concrete (RC) beam members. This study employed 1224 shear test specimens to train decision tree-based machine learning (ML) programs, by which strong correlations between shear capacity of RC beams and key input parameters were affirmed. In addition, shear contributions of concrete and shear reinforcement (the so-called Vc and Vs) were identified by establishing three independent ML models trained under different strategies with various combinations of datasets. Detailed parametric studies were then conducted by utilizing the well-trained ML models. It appeared that the presence of shear reinforcement can make the predicted shear contribution from concrete in RC beams larger than the pure shear contribution of concrete due to the intervention effect between shear reinforcement and concrete. On the other hand, the size effect also brought a significant impact on the shear contribution of concrete (Vc), whereas, the addition of shear reinforcements can effectively mitigate the size effect. It was also found that concrete tends to be the primary source of shear resistance when shear span-depth ratio a/d<1.0 while shear reinforcements become the primary source of shear resistance when a/d>2.0.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1048422).

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