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http://dx.doi.org/10.12989/cac.2017.20.2.247

Mechanical properties of blended cements at elevated temperatures predicted using a fuzzy logic model  

Beycioglu, Ahmet (Department of Civil Engineering, Technology Faculty, Duzce University)
Gultekin, Adil (Department of Civil Engineering, Technology Faculty, Duzce University)
Aruntas, Huseyin Yilmaz (Department of Civil Engineering, Technology Faculty, Gazi University)
Gencel, Osman (Department of Civil Engineering, Faculty of Engineering, Bartin University)
Dobiszewska, Magdalena (Department of Civil Engineering, Faculty of Civil and Environmental Engineering and Architecture, UTP University of Sciences and Technology)
Brostow, Witold (Laboratory of Advanced Polymers and Optimized Materials (LAPOM), Department of Materials Science and Eng. and Department of Physics, University of North Texas)
Publication Information
Computers and Concrete / v.20, no.2, 2017 , pp. 247-255 More about this Journal
Abstract
This study aimed to develop a Rule Based Mamdani Type Fuzzy Logic (RBMFL) model to predict the flexural strengths and compressive strengths of blended cements under elevated temperatures. Clinoptilolite was used as cement substitution material in the experimental stage. Substitution ratios in the cement mortar mix designs were selected as 0% (reference), 5%, 10%, 15% and 20%. The data used in the modeling process were obtained experimentally, after mortar specimens having reached the age of 90 days and exposed to $300^{\circ}C$, $400^{\circ}C$, $500^{\circ}C$ temperatures for 3 hours. In the RBMFL model, temperature ($C^{\circ}$) and substitution ratio of clinoptilolite (%) were inputs while the compressive strengths and flexural strengths of mortars were outputs. Results were compared by using some statistical methods. Statistical comparison results showed that rule based Mamdani type fuzzy logic can be an alternative approach for the evaluation of the mechanical properties of concrete under elevated temperature.
Keywords
blended cement; clinoptilolite; compressive strength; flexural strength; rule based fuzzy logic;
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Times Cited By KSCI : 4  (Citation Analysis)
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