مرکز منطقه ای اطلاع رساني علوم و فناوري - Forecastıng Compressıon Wıth Fuzzy Logıc Method On Dry Mix Concrete

Author/Authors :

ALKAN ÇAKIROĞLU, Melda Süleyman Demirel Üniversitesi - Teknik Eğitim Fakültesi - Yapı Eğitimi Bölümü, Turkey , TERZİ, Serdal Süleyman Demirel Üniversitesi, Çünür Kampüsü - Teknik Eğitim Fakültesi - Yapı Eğitimi Anabilim Dalı, Turkey , KASAP, Serdar Süleyman Demirel Üniversitesi - Teknik Eğitim Fakültesi - Yapı Eğitimi Bölümü, Turkey

Abstract :

There is no written method with the raotio of dry mix shocrete. To get a successful adhesion and compoction, it is the most importent point that the compressed air have enought. Flow and pressure. The lack of compressed air has negative effection adhesion,restitution and compressive strenght. To get a proper mixture with concrete agregate has to get moisture about % 3-6 ratio. The moisture is essential for prevention the occurance of dust during sputtery period. Containing much water in the mixture can cause blocky in transmission lain. Besides this, the amount of water entering to concrete is connected to the operator targeted compressive strength during application largely depending on these disadvantages are subject to change. Therefore, in this study is used from fuzzy logic with developed mathematical model method for find compression strength of dry mix concrete. For this purpose, prepared number of 3 squares 45 cm edges that suitable TS 11747 standard and number of 2 wooden panels that is 76cm and suitable ACI 506 standard for try dry mix concrete and find compression strength. Fuzzy logic with developed mathematical model method for find compression strength of dry mix concrete. Dry mix concrete that 10 cm thick is sprayed to test panels. Number of 9 cores specimens that slenderness rate 1 = (highness/calibre) = (100mm/100mm) = 1 and get panels is experimented test of compression strength. Prediction model of compression strength is developed with fuzzy logic method and length, calibre, area, broken of plummet of cores specimens. Datas of prediction model and datas of test are compared and reviewed. From the results observed close to quite is values estimate of the model with experimantal results.