dc.contributor.author | Öztürk, Murat | |
dc.contributor.author | Cansız, Ömer Faruk | |
dc.contributor.author | Sevim, Umur Korkut | |
dc.contributor.author | Bankir, Müzeyyen Balçıkanlı | |
dc.date.accessioned | 12.07.201910:50:10 | |
dc.date.accessioned | 2019-07-12T22:06:11Z | |
dc.date.available | 12.07.201910:50:10 | |
dc.date.available | 2019-07-12T22:06:11Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Ozturk, M., Cansiz, O.F., Sevim, U.K., Balcikanli Bankir, M. (2018). MLR & ANN approaches for prediction of compressive strength of alkali activated EAFS
Computers and Concrete, 21 (5), pp. 559-567.
https://doi.org/10.12989/cac.2018.21.5.559 | en_US |
dc.identifier.issn | 1598-8198 | |
dc.identifier.issn | 1598-818X | |
dc.identifier.uri | https://doi.org/10.12989/cac.2018.21.5.559 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12508/661 | |
dc.description | WOS: 000433095400009 | en_US |
dc.description.abstract | 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 degrees C-800 degrees 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Techno Press | en_US |
dc.relation.isversionof | 10.12989/cac.2018.21.5.559 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Alkali activation | en_US |
dc.subject | Electrical arc furnace slag | en_US |
dc.subject | Regression | en_US |
dc.subject | ANN | en_US |
dc.subject.classification | Computer Science | en_US |
dc.subject.classification | Interdisciplinary Applications | en_US |
dc.subject.classification | Construction & Building Technology | en_US |
dc.subject.classification | Engineering | en_US |
dc.subject.classification | Civil | en_US |
dc.subject.classification | Materials Science | en_US |
dc.subject.classification | Characterization & Testing | en_US |
dc.subject.classification | Basic Oxygen Converter | Slag | Arc Furnace | en_US |
dc.subject.other | Steel slag | en_US |
dc.subject.other | Silica fume | en_US |
dc.subject.other | Concrete | en_US |
dc.subject.other | Aggregate | en_US |
dc.subject.other | Performance | en_US |
dc.subject.other | Durability | en_US |
dc.subject.other | Chemical activation | en_US |
dc.subject.other | Curing | en_US |
dc.subject.other | Electric arcs | en_US |
dc.subject.other | Electric furnaces | en_US |
dc.subject.other | Linear regression | en_US |
dc.subject.other | Neural networks | en_US |
dc.subject.other | Oxygen vacancies | en_US |
dc.subject.other | Silicates | en_US |
dc.subject.other | Slags | en_US |
dc.subject.other | Sodium | en_US |
dc.subject.other | Software testing | en_US |
dc.subject.other | Alkali activation | en_US |
dc.subject.other | Artificial neural network models | en_US |
dc.subject.other | Electric arc furnace slags | en_US |
dc.subject.other | Electrical arc furnaces | en_US |
dc.subject.other | Humidity conditions | en_US |
dc.subject.other | Multiple linear regressions | en_US |
dc.subject.other | Regression | en_US |
dc.subject.other | Regression equation | en_US |
dc.subject.other | Compressive strength | en_US |
dc.title | MLR & ANN approaches for prediction of compressive strength of alkali activated EAFS | en_US |
dc.type | article | en_US |
dc.relation.journal | Computers and Concrete | en_US |
dc.contributor.department | Mühendislik ve Doğa Bilimleri Fakültesi -- İnşaat Mühendisliği Bölümü | en_US |
dc.identifier.volume | 21 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.startpage | 559 | en_US |
dc.identifier.endpage | 567 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.contributor.isteauthor | Öztürk, Murat | en_US |
dc.contributor.isteauthor | Cansız, Ömer Faruk | en_US |
dc.contributor.isteauthor | Sevim, Umur Korkut | en_US |
dc.contributor.isteauthor | Bankir, Müzeyyen Balçıkanlı | |
dc.relation.index | Web of Science - Scopus | en_US |
dc.relation.index | Web of Science Core Collection - Science Citation Index Expanded | en_US |