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http://dx.doi.org/10.4334/JKCI.2016.28.6.723

Prediction of Transfer Lengths in Pretensioned Concrete Members Using Neuro-Fuzzy System  

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)
Publication Information
Journal of the Korea Concrete Institute / v.28, no.6, 2016 , pp. 723-731 More about this Journal
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.
Keywords
ANFIS; fuzzy; transfer length; neuro-fuzzy; pretensioned concrete member;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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