Computers and Concrete

  • 제23권1호
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  • Pages.49-60
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  • 2019
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Determining the shear strength of FRP-RC beams using soft computing and code methods

  • Yavuz, Gunnur (Department of Civil Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University)
  • 투고 : 2018.04.09
  • 심사 : 2019.01.22
  • 발행 : 2019.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

In recent years, multiple experimental studies have been performed on using fiber reinforced polymer (FRP) bars in reinforced concrete (RC) structural members. FRP bars provide a new type of reinforcement that avoids the corrosion of traditional steel reinforcement. In this study, predicting the shear strength of RC beams with FRP longitudinal bars using artificial neural networks (ANNs) is investigated as a different approach from the current specific codes. An ANN model was developed using the experimental data of 104 FRP-RC specimens from an existing database in the literature. Seven different input parameters affecting the shear strength of FRP bar reinforced RC beams were selected to create the ANN structure. The most convenient ANN algorithm was determined as traingdx. The results from current codes (ACI440.1R-15 and JSCE) and existing literature in predicting the shear strength of FRP-RC beams were investigated using the identical test data. The study shows that the ANN model produces acceptable predictions for the ultimate shear strength of FRP-RC beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model provides more accurate predictions for the shear capacity than the other computed methods in the ACI440.1R-15, JSCE codes and existing literature for considering different performance parameters.

키워드

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피인용 문헌

  1. Prediction of concrete spall damage under blast: Neural approach with synthetic data vol.26, pp.6, 2019, https://doi.org/10.12989/cac.2020.26.6.533