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Title |
Correlation between Mix Proportion and Mechanical Characteristics of Steel Fiber Reinforced Concrete
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Authors |
최현기(Hyun-Ki Choi) ; 배백일(Baek-Il Bae) ; 구해식(Hae-Shik Koo) |
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DOI |
http://dx.doi.org/10.4334/JKCI.2015.27.4.331 |
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Keywords |
섬유보강 콘크리트 ; 배합비 ; 압축강도 ; 인장강도 ; 인공신경망 Steel Fiber reinforced Concrete ; mix proportion ; compressive strength ; tensile strength ; artificial neural network |
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Abstract |
The main purpose of this study is reducing the cost and effort for characterization of tensile strength of fiber reinforced concrete, in order to use in structural design. For this purpose, in this study, test for fiber reinforced concrete was carried out. Because fiber reinforced concrete is consisted of diverse material, it is hard to define the correlation between mix proportions and strength. Therefore, compressive strength test and tensile strength test were carried out for the range of smaller than 100 MPa of compressive strength and 0.25~1% of steel fiber volume fraction. as a results of test, two types of tensile strength were highly affected by compressive strength of concrete. However, increase rate of tensile strength was decreased with increase of compressive strength. Increase rate of tensile strength was decreased with increase of fiber volume fraction. Database was constructed using previous research data. Because estimation equations for tensile strength of fiber reinforced concrete should be multiple variable function, linear regression is hard to apply. Therefore, in this study, we decided to use the ANN(Artificial Neural Network). ANN was constructed using multiple layer perceptron architecture. Sigmoid function was used as transfer function and back propagation training method was used. As a results of prediction using artificial neural network, predicted values of test data and previous research which was randomly selected were well agreed with each other. And the main effective parameters are water-cement ratio and fiber volume fraction.
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