Abstract

Using recycled aggregates from construction and demolition waste can preserve natural aggregate resources, reduce demand of landfill, and contribute to sustainable built environment. This study provides a comprehensive review on recycled aggregate (RA) and recycled aggregate concrete (RAC) regarding their history, recycling, reuse and manufacture process, inherent defects (e.g. existing of additional interfacial transition zones in RAC), and materials properties. Specifically, these properties of RAC include fresh concrete workability, physical and chemical properties (i.e. density, carbonation depth, and chloride ion penetration), mechanical properties (i.e. compressive, flexural, and splitting tensile strength as well as elastic modulus), and long-term performance (i.e. freezing-thawing resistance, alkali-silica reaction resistance, creep, and dry shrinkage). On top of that, methods for improving RAC mechanical properties and long-term performance are summarized and categorized into three groups, i.e. (1) reduction of recycled aggregate porosity, (2) reduction of old mortar layer on recycled aggregate surface, and (3) property improvement without recycled aggregate modification (i.e. different concrete mixing design and addition of fibre reinforcement). Next, current regression-based models and artificial intelligence models on the prediction of compressive strength, modulus, and compressive stress-strain curves of RAC are reviewed and their limitations of those models are discussed. Furthermore, the state-of-the-art RAC applications are presented. Additionally, challenges of RAC application are reviewed taking China as an example. The link between material from CDW and EU green policy are discussed by analysing the previous research projects funded by European Commission. Finally, future perspectives of RAC research focus are discussed, i.e. development of “green” treatment methods on recycled aggregates, further direction on nanoparticle application in RAC, and the establishment of database for RAC strength prediction.

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