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논문 기본 정보

자료유형
학술저널
저자정보
Murat Ozturk (Iskenderun Technical University) Omer F. Cansiz (Iskenderun Technical University) Umur K. Sevim (Iskenderun Technical University) Muzeyyen Balcikanli Bankir (Iskenderun Technical University)
저널정보
한국계산역학회 Computers and Concrete, An International Journal Computers and Concrete, An International Journal Vol.21 No.5
발행연도
2018.1
수록면
559 - 567 (9page)

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In this study alkali activation of Electric Arc Furnace Slag (EAFS) is studied with a comprehensive test program. Three different silicate moduli (1-1,5-2), three different sodium concentrations (4%-6%-8%) for each silicate module, two different curing conditions (45%-98% relative humidity) for each sodium concentration, two different curing temperatures (400°C-800°C) for each relative humidity condition and two different curing time (6h-12h) for each curing temperature variables are selected and their effects on compressive strength was evaluated then regression equations using multiple linear regressions methods are fitted. And then to select the best regression models confirm with using the variables, the regression models compared between itself. An Artificial Neural Network (ANN) models that use silicate moduli, sodium concentration, relative humidity, curing temperature and curing time variables, are formed. After the investigation of these ANN models’ results, ANN and multiple linear regressions based models are compared with each other. After that, an explicit formula is developed with values of the ANN model. As a result of this study, the fluctuations of data set of the compressive strength were very well reflected using both of the methods, multiple linear regression with quadratic terms and ANN.

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