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

• The developed artificial neural network allow accurate prediction of the compressive strength for CO 2 Concrete. • The artificial neural network exhibited a strong relationship with the experimental specimens. • The artificial neural network was also validated by 22 laboratory validation concrete mixes. • The successful prediction of compressive strength of CO 2 Concrete can help a greater mainstream use of the green material. Concrete is a very effective material for the construction of buildings and infrastructure around the world. Unfortunately, typical concrete is a large contributor to CO 2 emissions and consumption of natural reserves. CO 2 Concrete allows the mitigation of these downfalls by carbonating recycled aggregate, reducing CO 2 emissions, reusing crushed masonry materials and conserving virgin aggregate. CO 2 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 CO 2 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 CO 2 Concrete can help a greater mainstream use of the green material. [ABSTRACT FROM AUTHOR]