earticle

영어

Demolition waste generation has exceedingly increased in the world, including korea. It is estimated the approximately 115 thousand tons of waste concrete are occurred in 2009 in this country. And it is estimated that the waste concrete will be more generated more each year than those of today. Therefore, it is necessary to use waste concrete for aggregate of concrete. Many studies have been conducted to reuse of recycled aggregate for construction materials. To make use of recycled aggregate, the elastic modulus of recycled aggregate concrete must bo assured. The purpose of this study is to predict elastic modulus of recycled aggregate concrete using neural network which was applied to predict properties of engineering problems. New learning algorithms for the prediction on the elastic modulus of recycled aggregate concrete is proposed focusing on input layer components and a normalization method for input data and their validity is examined through elastic modulus data of recycled aggregate concrete. In artificial neural network algorithm, the main input data to be trained are the water content, fine and coarse aggregate ratios, curing temperature of recycled aggregate concrete, water/cement ratios and etc. Through this study, it was confirmed that artificial neural network successfully predict the elastic modulus of recycled aggregate concrete.