Optimizing Recycled Aggregate Concrete for Severe Conditions Through Machine Learning Techniques: A Review
Abstract
This critical review investigates the use of recycled concrete aggregates (RCA) in concrete structures under the harsh conditions of aggressive environments, emphasizing sustainable construction practices. Recycled concrete stands out as a key solution, especially in severe and coastal climates, due to its enhanced durability and sustainability. Based on the previous work in literature, it is shown that concrete structures built with recycled concrete exhibit a 30% increase in durability against saltwater corrosion compared to those constructed with traditional concrete. Qualitative evaluations highlight its architectural versatility, adapting effectively to various environmental conditions. Life-Cycle Cost Analysis (LCCA) indicates a substantial 25% reduction in long-term maintenance costs for these structures. Additionally, the environmental impact assessment shows a 40% decrease in carbon footprint and a 20% reduction in water and energy usage, affirming the material's ecological benefits. Case studies underscore the increased design flexibility, presenting more resilient and sustainable construction options. The review culminates in a discussion of the challenges faced by this emerging material, paving the way for future research. This novel addition aims to diversify construction materials, offering a more sustainable alternative for infrastructure development by optimizing the mix design of recycled aggregate concrete (RAC) through Machine learning (ML) techniques. In summary, this research aspires to fill critical research gaps, offering a comprehensive and innovative approach to utilizing recycled aggregates in high-performance concrete, ultimately contributing to sustainable construction practices in terms of processing of RAC which can be predicted through ML techniques.