Recycled concrete has emerged as a sustainable alternative to traditional construction materials due to its reduced environmental impact and cost-effectiveness.
The physical and mechanical properties of recycled concrete, however, can vary significantly, making it challenging to ensure consistent quality.
Machine learning (ML) techniques have been increasingly applied to predict and optimize the properties of recycled concrete.
This chapter discusses the various types of ML algorithms used in this context, such as artificial neural networks, support vector machines, decision trees, and random forests.
We also examine the various properties of recycled concrete that have been studied with ML, including compressive strength, modulus of elasticity, porosity, and durability.
By using ML techniques, researchers and engineers can optimize the composition of recycled concrete and ensure that it meets the necessary performance requirements.