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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국계산역학회 Computers and Concrete, An International Journal Computers and Concrete, An International Journal Vol.20 No.1
- 발행연도
- 2017.1
- 수록면
- 31 - 38 (8page)
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초록· 키워드
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In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN’s are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.
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참고문헌 (31)
참고문헌 신청
Acikgenc, M. / 2015 / Using an artificial neural network to predict mix compositions of steel fiber-reinforced concrete / Arab J. Sci. Eng. 40(2):407~419
Arabi N. S. Alqadi / 2012 / Uses of Central Composite Design and Surface Response to Evaluate the Influence of Constituent Materials on Fresh and Hardened Properties of Self-Compacting Concrete / KSCE Journal of Civil Engineering 16(3):407~416
Atici, U. / 2011 / Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network / Exp. Syst. Appl. 38(8):9609~9618
Bondar, D. / 2014 / Use of a neural network to predict strength and optimum compositions of natural alumina-silica-based geopolymers / J. Mater. Civil Eng. 26(3):499~503
Cahit, B. / 2009 / Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network / Adv. Eng. Soft. 40(5):334~340
Arabi N. S. Alqadi / 2012 / Uses of Central Composite Design and Surface Response to Evaluate the Influence of Constituent Materials on Fresh and Hardened Properties of Self-Compacting Concrete / KSCE Journal of Civil Engineering 16(3):407~416
Atici, U. / 2011 / Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network / Exp. Syst. Appl. 38(8):9609~9618
Bondar, D. / 2014 / Use of a neural network to predict strength and optimum compositions of natural alumina-silica-based geopolymers / J. Mater. Civil Eng. 26(3):499~503
Cahit, B. / 2009 / Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network / Adv. Eng. Soft. 40(5):334~340
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