ESTIMATION OF COMPRESSIVE STRENGTH OF WASTE MARBLE DUST AND GLASS FIBRE INCLUDED CONCRETES BY ARTIFICIAL NEURAL NETWORK

Abstract

In this study, during the production phase of 300 and 350 dosages concretes that has 0.60 water/cement ratio, clipped glass fibre added to the mixture in ratio of four including the filler material and 25, 50, 75 and 100% volume ratio waste marble dust which can be used in place of filler material. Compressive strength, splitting tensile strength, ultrasonic pulse velocity and porosity coefficient of obtained sample were determined. Approximate estimation of compressive strength performed via selecting dosages, fibre ratio, marble dust ratio, porosity coefficient, ultrasonic pulse velocity and splitting tensile strength as an input of Artificial Neural Network (ANN). As a result of this study, developed ANN model compared with the obtained experimental values and it was detected that results match with each other.

Keywords

• Glass Fibre, Waste Marble Dust, Concrete, Artificial Neural Network, Compressive Strength,

ATIK MERMER TOZU VE CAM LYF KATKILI BETONLARIN BASINÇ DAYANIMLARININ YAPAY SYNYR A?LARI YLE TAHMYNY

Öz

Bu çaly?mada, su/çimento orany 0,60 olan, 300 ve 350 dozlu betonlaryn üretimi syrasynda kary?yma 4 farkly oranda kyrpylmy? cam lif ilave edilerek elde edilen bu serilere filler malzeme ile hacimce %25, 50, 75 ve 100 oranlarynda yer de?i?tirecek ?ekilde atyk mermer tozu ilave edildi. Elde edilen numunelerin ultrases geçi? hyzlary, porozite de?erleri, yarmada çekme dayanymlary ve basynç dayanymlary belirlendi. Yapay sinir a?yna (YSA) girdi seti olarak dozaj, agrega miktary, lif orany, mermer tozu orany, porozite, ultrases geçi? hyzy ve yarmada çekme dayanymy de?erleri seçilerek basynç dayanymlary tahmin edildi. Çaly?ma sonucunda, geli?tirilen YSA modeli ile deneysel olarak elde edilen veriler kar?yla?tyryldy ve sonuçlaryn uyum içerisinde oldu?u belirlendi.

Anahtar Kelimeler

• Cam Lif, Atyk Mermer Tozu, Beton, Yapay Sinir A?y, Basynç Dayanymy,