Prediction model for elastic modulus of recycled concrete based on properties of recycled coarse aggregate and cementitious materials

• Published in: Case Studies in Construction Materials 2024-12, Vol.21, p.e04058, Article e04058
• Main Authors: Liu, Xu, Zhang, Xiaohang, Yan, Pengpeng
• Format: Article
• Language: English
• Subjects: Elastic modulus; Prediction model; Recycled concrete; Supplementary cementitious materials
• ISSN: 2214-5095; 2214-5095
• DOI: 10.1016/j.cscm.2024.e04058

The supplementary cementitious materials (SCMs) are commonly used in recycled coarse aggregate (RA) concrete to improve the performance of RA concrete or reduce the amount of cement used. However, the diverse sources and compositions of RA and SCMs pose a challenge for predicting the elastic modulus of concrete containing both RA and SCMs. In this study, the main influencing factors on the elastic modulus of recycled concrete were analyzed, and the statistical significance of the influencing factors on the elastic modulus was quantitatively analyzed using factor analysis techniques, either individually or in interaction. The results of the factor analysis reveled that the content of recycled coarse aggregates and the reactivity index of the cementitious materials have the most significant impact on the elastic modulus, followed by the water absorption of recycled coarse aggregates and the ratio of aggregates to cementitious materials (A/C). Furthermore, the interaction between A/C ratio and the content of recycled coarse aggregates, as well as the interaction between the water absorption and the content of recycled coarse aggregates, were also found to be significant influencing factors on the elastic modulus. A unified prediction model for elastic modulus was proposed by considering the hydraulic and pozzolanic activities of OPC and SCMs, performance and content of recycled coarse aggregate simultaneously, based on a statistical analysis of the experimental database. The statistical fitting results indicted that the elastic modulus can be accurately predicted using proposed model, thus promoting the application of recycled aggregate and green binder in structures. [Display omitted]