Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Author: search.filters.author.Kırca, ÖnderPublication Index: search.filters.publicationIndex.Scopus

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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; Tayfur, Gökmen; Kırca, Önder
    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: 54
    Citation - Scopus: 69
    Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks
    (American Society of Civil Engineers (ASCE), 2014) Tayfur, Gökmen; Erdem, Tahir Kemal; Kırca, Önder
    High-strength concretes (HSC) were prepared with five different binder contents, each of which had several silica fume (SF) ratios (0-15%). The compressive strength was determined at 3, 7, and 28 days, resulting in a total of 60 sets of data. In a fuzzy logic (FL) algorithm, three input variables (SF content, binder content, and age) and the output variable (compressive strength) were fuzzified using triangular membership functions. A total of 24 fuzzy rules were inferred from 60% of the data. Moreover, the FL model was tested against an artificial neural networks (ANNs) model. The results show that FL can successfully be applied to predict the compressive strength of HSC. Three input variables were sufficient to obtain accurate results. The operators used in constructing the FL model were found to be appropriate for compressive strength prediction. The performance of FL was comparable to that of ANN. The extrapolation capability of FL and ANNs were found to be satisfactory.