Modeling slump flow of high-performance concrete using a back-propagation network

Abstract

This paper proposes a back-propagated network (BPN) and applies it to estimate the slump flow of high-performance concrete (HPC). HPC is a highly complex material whose behavior is difficult to model, especially slump flow. Slump flow estimation is a function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. BPN is a well-known method, used to automatically discover the complex relationships among nonlinear systems. The results show that BPN predicts the slump flow of HPC with satisfyied estimating errors.