WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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2011 - 20143

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  • Article
    Citation - WoS: 5
    Citation - Scopus: 7
    Experimental and Modeling Study of Strength of High Strength Concrete Containing Binary and Ternary Binders
    (Foundation Cement, Lime, Concrete, 2011) Erdem, Tahir Kemal; Erdem, Tahir Kemal; Kırca, Önder; Tayfur, Gökmen; 03.03. Department of Civil Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Silica fume (SF), fl y ash (FA) and ground granulated blastfurnace slag (S) are among the most widely utilized mineral additions for normal strength concrete (NSC) and high strength concrete (HSC). High Reactivity Metakaolin (HRMK) is a relatively new mineral addition, produced by calcination of highly pure kaolin. The replacement of cement with HRMK increases the strength, especially at early ages, and improves durability of concrete. (1-3). Pumice (P) is a porous volcanic glass containing 60-75 SiO2% and 13-17% Al2O3. When fi nely ground, it shows pozzolanic characteristics but it is generally used as a lightweight aggregate in the concrete industry (4, 5). HRMK and P have white color and, therefore, are useful for production of white concrete when applied with white Portland cement (WPC)
  • Article
    Citation - WoS: 96
    Citation - Scopus: 105
    Comparative Study of a Building Energy Performance Software (kep-Iyte and Ann-Based Building Heat Load Estimation
    (Elsevier Ltd., 2014) Turhan, Cihan; Kazanasmaz, Zehra Tuğçe; Kazanasmaz, Zehra Tuğçe; Turhan, Cihan; Erlalelitepe Uygun, İlknur; Gökçen Akkurt, Gülden; Ekmen, Kenan Evren; Gökçen Akkurt, Gülden; 02.02. Department of Architecture; 03.10. Department of Mechanical Engineering; 03.06. Department of Energy Systems Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology; 02. Faculty of Architecture
    The several parameters affect the heat load of a building; geometry, construction, layout, climate and the users. These parameters are complex and interrelated. Comprehensive models are needed to understand relationships among the parameters that can handle non-linearities. The aim of this study is to predict heat load of existing buildings benefiting from width/length ratio, wall overall heat transfer coefficient, area/volume ratio, total external surface area, total window area/total external surface area ratio by using artificial neural networks and compare the results with a building energy simulation tool called KEP-IYTE-ESS developed by Izmir Institute of Technology. A back propagation neural network algorithm has been preferred and both simulation tools were applied to 148 residential buildings selected from 3 municipalities of Izmir-Turkey. Under the given conditions, a good coherence was observed between artificial neural network and building energy simulation tool results with a mean absolute percentage error of 5.06% and successful prediction rate of 0.977. The advantages of ANN model over the energy simulation software are observed as the simplicity, the speed of calculation and learning from the limited data sets.
  • Article
    Citation - WoS: 35
    Citation - Scopus: 36
    Coupling Soil Moisture and Precipitation Observations for Predicting Hourly Runoff at Small Catchment Scale
    (Elsevier Ltd., 2014) Tayfur, Gökmen; Tayfur, Gökmen; Brocca, Luca; Moramarco, Tommaso; 03.03. Department of Civil Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    The importance of soil moisture is recognized in rainfall-runoff processes. This study quantitatively investigates the use of soil moisture measured at 10, 20, and 40cm soil depths along with rainfall in predicting runoff. For this purpose, two small sub-catchments of Tiber River Basin, in Italy, were instrumented during periods of October 2002-March 2003 and January-April 2004. Colorso Basin is about 13km2 and Niccone basin 137km2. Rainfall plus soil moisture at 10, 20, and 40cm formed the input vector while the discharge was the target output in the model of generalized regression neural network (GRNN). The model for each basin was calibrated and tested using October 2002-March 2003 data. The calibrated and tested GRNN was then employed to predict runoff for each basin for the period of January-April 2004. The model performance was found to be satisfactory with determination coefficient, R2, equal to 0.87 and Nash-Sutcliffe efficiency, NS, equal to 0.86 in the validation phase for both catchments. The investigation of effects of soil moisture on runoff prediction revealed that the addition of soil moisture data, along with rainfall, tremendously improves the performance of the model. The sensitivity analysis indicated that the use of soil moisture data at different depths allows to preserve the memory of the system thus having a similar effect of employing the past values of rainfall, but with improved GRNN performance.