지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국계산역학회 Computers and Concrete, An International Journal Computers and Concrete, An International Journal Vol.22 No.2
- 발행연도
- 2018.1
- 수록면
- 249 - 259 (11page)
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초록· 키워드
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This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi- Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.
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참고문헌 (33)
참고문헌 신청
Adhikary, B.B. / 2004 / Artificial neural networks for the prediction of shear capacity of steel plate strengthened RC beams / Constr. Build. Mater. 18:409~417
Ahmad, S. / 2011 / Seismic vulnerability of non-ductile reinforced concrete structures in developing countries
Bal, I.E. / 2008 / Detailed assessment of structural characteristics of Turkish RC building stock for loss assessment models / Soil Dyn. Earthq. Eng. 28(10):914~932
M. Bilgehan / 2010 / The use of neural networks in concrete compressive strength estimation / Computers and Concrete, An International Journal 7(3):271~283
Dahoua, Z. / 2009 / Artificial Neural Network model for steel-concrete bond Prediction / Eng. Struct. 31:1724~1733
Ahmad, S. / 2011 / Seismic vulnerability of non-ductile reinforced concrete structures in developing countries
Bal, I.E. / 2008 / Detailed assessment of structural characteristics of Turkish RC building stock for loss assessment models / Soil Dyn. Earthq. Eng. 28(10):914~932
M. Bilgehan / 2010 / The use of neural networks in concrete compressive strength estimation / Computers and Concrete, An International Journal 7(3):271~283
Dahoua, Z. / 2009 / Artificial Neural Network model for steel-concrete bond Prediction / Eng. Struct. 31:1724~1733
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