Properties prediction of environmentally friendly ultra-high-performance concrete using artificial neural networks

Abstract

Ultra-high-performance concrete (UHPC) results from the mixture of several constituents, leading to a highly complex material in both, fresh and hardened state. The higher number of constituents, together with a higher number of possible combinations, relative proportioning and characteristics, makes the behavior of this type of concrete more difficult to predict. The objective of the research is to build four analytical models, based on artificial neural networks (ANN), to predict the 1-day, 7-day, and 28-day compressive strengths and slump flow. Recycled glass powder milled to different particle size, fluid catalytic cracking residue (FCC) and different particle size limestone powder was used as partial replacements for Portland cement and silica fume. The ANN models predicted the 1-day, 7-day, and 28-day compressive strengths and slump flow of the test set with prediction error values (RMSE) of 2.400 MPa, 2.638 MPa, 2.064 MPa and 7.245 mm respectively. The results indicated that the developed ANN models are an efficient tool for predicting the slump flow and compressive strengths of UHPC while incorporating silica fume, limestone powder, recycled glass powder and FCC.