Optimized Light Weight Light Emitting Concret...

Optimized Light Weight Light Emitting Concrete Blocks

Author Name : D.Gomathi, S.Pavithra, S.K.Sasepriya, Mrs.Dr.Maragatham.S

ABSTRACT

Construction industry is one the industry which use maximum amount of natural resource for their construction activity. Sustainability of natural resources can be maintained or kept in existence by replacing natural resources with renewable or manmade resources like M-sand, recycled aggregate and some other materials. According to Environment Protection Agency (EPA), Green House Gas (GHG) emission from electricity increased by about 18%. Hence to reduce the usage of electricity, optic fibers is used in concrete structures. In construction process, predicting the compressive strength of concrete is difficult, since the concrete is sensitive to the mixture of components, curing conditions, compacting, method of mixing etc. scientists had proposed different methods to predict the strength of concrete. In that some of the methods get succeed. The aim of this study is to predict the optimum concrete mix with partial replacement of cement and fine aggregate with Ground Granulated Blast furnace Slag (GGBS) and biochar; to attain the optimum mix proportion along with optic fibers and compressive  strength of concrete using Artificial Neural Network(ANN) in an effective manner. So therefore considering some specific concrete characteristics as input variables and constructing an Artificial Neural Network model, the compressive strength of concrete is predicted. Therefore the results show that ANN is a suitable method to predict the 28days compressive strength of concrete.

Keywords:- Concrete, Artificial Neural Network, partial replacement, Compressive strength.