A prediction model for compressive strength of CO2 concrete using regression analysis and artificial neural networks

Abstract

Concrete is a very effective material for the construction of buildings and infrastructure around the world. Unfortunately, typical concrete is a large contributor to CO2 emissions and consumption of natural reserves. CO2 Concrete allows the mitigation of these downfalls by carbonating recycled aggregate, reducing CO2 emissions, reusing crushed masonry materials and conserving virgin aggregate. CO2 Concrete can also be considered reliable as its compressive strength can be accurately predicted by both regression analysis and artificial neural networks. The artificial neural network created for this paper allow accurate prediction of the compressive strength for CO2 Concrete. The artificial neural network exhibited a strong relationship with the experimental specimens, revealing a multiple R of 0.98 and an R square of 0.95. The artificial neural network was also validated by 22 laboratory validation concrete mixes. The artificial neural network displayed an average error of 1.24 MPa or 3.43% in the validation mixes with 59% of concrete samples within 3% error and 77% being within 5% error. The successful prediction of compressive strength of CO2 Concrete can help a greater mainstream use of the green material.