6 - Applying artificial intelligence to predict green concrete compressive strength

Abstract

Concrete is the world's most commonly used construction material. Nevertheless, the processing of Portland cement, a major component of concrete, contributes to substantial CO2 emissions and other greenhouse gases (GHGs). Moreover, the energy demands of sustainable concrete must be very minimal, generate low wastes, use some of the abundantly available products on Earth. The buildings must be durable, exhibit high thermal mass, and be produced from wastes or recyclable materials. Sustainable concrete buildings should also have a limited environmental impact. Recycled aggregate concrete (RAC), given these parameters, can be used to protect the environment while including sustainable concrete as an alternative source. Pozzolanic compounds are used as partial cement substitutes in the most advanced concrete mixes and blend the benefits of improved capabilities, lower GHG emissions, low fuel consumption in the final processing of concrete, and particulate matters (PM) recycling. The compressive intensity of such a concrete mix, however, is regarded as a primary constraint and makes its use a significant concern for construction professionals. This chapter discusses how specific environmental effects can be reduced and how concrete efficiency can be measured and improved. This applies to the most sustainable benefits of using green concrete. The chapter reviews the basic concepts of the mix designs of green concrete (i.e., RAC) and extensively examines the effect of concrete compositions on its compressive strength and mechanical properties. This chapter also reviews the compressive strength of RAC using artificial intelligence (AI) methods such as artificial neural networks, multiple linear regression, the adaptive neuro-fuzzy inference system, and others. AI-based models have shown strong potential for predicting the mechanical characteristics and performance of recycled concrete.