Journal of the Korea Concrete Institute (콘크리트학회논문집)

Korea Concrete Institute (한국콘크리트학회)
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Prediction of Transfer Lengths in Pretensioned Concrete Members Using Neuro-Fuzzy System

뉴로-퍼지 시스템을 이용한 프리텐션 콘크리트 부재의 전달길이 예측

  • Kim, Minsu (Department of Architectural Engineering, University of Seoul) ;
  • Han, Sun-Jin (Department of Architectural Engineering, University of Seoul) ;
  • Cho, Hae-Chang (Department of Architectural Engineering, University of Seoul) ;
  • Oh, Jae-Yuel (Department of Architectural Engineering, University of Seoul) ;
  • Kim, Kang Su (Department of Architectural Engineering, University of Seoul)
  • 김민수 (서울시립대학교 건축학부) ;
  • 한선진 (서울시립대학교 건축학부) ;
  • 조해창 (서울시립대학교 건축학부) ;
  • 오재열 (서울시립대학교 건축학부) ;
  • 김강수 (서울시립대학교 건축학부)
  • Received : 2016.08.03
  • Accepted : 2016.10.19
  • Published : 2016.12.30
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Abstract

In pretensioned concrete members, a certain bond length from the end of the member is required to secure the effective prestress in the strands, which is defined as the transfer length. However, due to the complex bond mechanism between strands and concrete, most transfer length models based on the deterministic approach have uncertainties and do not provide accurate estimations. Therefore, in this study, Adaptive Neuro-Fuzzy Inference System (ANFIS), a Neuro-Fuzzy System, is introduced to reduce the uncertainties and to estimate the transfer length more accurately in pretensioned concrete member. A total of 253 transfer length test results have been collected from literatures to train ANFIS, and the trained ANFIS algorithm estimated the transfer length very accurately. In addition, a design equation was proposed to calculate the transfer length based on parametric studies and dimensional analyses. Consequently, the proposed equation provided accurate results on the transfer length which are comparable to the ANFIS analysis results.

프리텐션 콘크리트 부재에서 강연선의 유효프리스트레스를 확보하기 위해서는 부재의 단부부터 특정 부착길이가 필요하며, 이를 전달길이라고 정의한다. 그러나, 강연선과 콘크리트 사이의 복잡한 부착 메커니즘으로 인해 결정론적인 방법으로 전달길이를 산정하는 기존 방법들은 많은 불확실성을 내포하고 있으며, 안전측의 해석결과를 제공하는 것에 초점이 맞추어져 있다. 따라서, 이 연구에서는 여러 영향인자들의 복잡한 메커니즘을 보다 효과적으로 고려하여 정확한 전달길이를 산정하기 위해 뉴로-퍼지 시스템의 방법 중 하나인 ANFIS를 도입하였다. 기존 연구로부터 총 253개의 실험체를 수집하여 ANFIS 알고리즘을 훈련시켰으며, 훈련된 ANFIS 알고리즘은 전달길이를 매우 정확히 예측하였다. 또한, ANFIS 전달길이 평가결과를 토대로, 변수분석과 차원해석을 수행하여 보다 간략화된 전달길이 산정식을 제안하였으며, 제안식은 ANFIS 해석결과와 거의 대등한 정확도를 보여주었다.

Keywords

References

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