Performance Assessment of One-Part Self-Compacted Geopolymer Concrete Containing Recycled Concrete Aggregate: A Critical Comparison Using Artificial Neural Network (ANN) and Linear Regression Models

Abstract

Geopolymer concrete, a cement-free concrete with recycled concrete aggregate (RCA), offers an eco-friendly solution for reducing carbon emissions from cement production and reusing a significant amount of old concrete from construction and demolition waste. This research on self-compacted, ambient-cured, and low-carbon concrete demonstrates the superior performance of one-part geopolymer concrete made from recycled materials. It is achieved by optimally replacing treated RCA with a unique method that involves coating the recycled aggregates with a one-part geopolymer slurry composed of fly ash, micro fly ash, slag, and anhydrous sodium metasilicate. The research presented in this paper introduces predictive models to assist researchers in optimising concrete mix designs based on RCA rates and treatment methods, including the incorporation of coated recycled concrete aggregates and basalt fibres. This study addresses the knowledge gap regarding geopolymer concrete based on recycled aggregate, various RCA rates, and novel RCA treatments. The novelty of the paper also lies in presenting the effectiveness of Artificial Neural Network (ANN) models in accurately predicting the compressive strength, splitting tensile strength, and modulus of elasticity for self-compacting geopolymer concrete with various rates of RCA replacement. This addresses a knowledge gap in existing research on ANN models for the prediction of geopolymer concrete properties based on RCA rate and treatment. The ANN models developed in this research predict results that are more comparable to experimental outcomes, showcasing superior accuracy compared to linear regression models.

Keywords

• artificial neural network
• low carbon concrete
• geopolymer concrete
• recycled concrete
• green construction material