Optimization of Concrete Mix Design Parameters through Artificial Intelligence Techniques

Abstract

Concrete is one of the most widely used construction materials globally, and the composition of its mix design heavily influences its performance. Traditional concrete mix design methods often rely on empirical approaches, which can be time-consuming and may not yield optimal results. With Artificial Intelligence (AI) advancements, there is a growing potential to enhance concrete mix design through data-driven methodologies. This review paper explores the application of various AI techniques, including machine-learning algorithms, genetic algorithms, and neural networks, in optimizing concrete mix formulations. It discusses the efficacy of these methods in predicting concrete properties, such as compressive strength, workability, and durability, while also addressing the sustainability aspects of mix design. By integrating AI-driven approaches, the construction industry can achieve more efficient use of materials, reduce waste, and minimize environmental impacts. The findings highlight the transformative role of AI in revolutionizing concrete mix design, ultimately leading to improved performance and sustainability in construction practices.