Porosity-based models for estimating the mechanical properties of self-compacting concrete with coarse and fine recycled concrete aggregate

Predicting the mechanical properties of Self-Compacting Concrete (SCC) containing Recycled Concrete Aggregate (RCA) generally depends, in great part, on the RCA fraction in use. In this study, predictive equations for estimating SCC mechanical properties are developed through SCC porosity indices, so they are applicable to any RCA fraction and amount that may be used. A total of ten SCC mixes were prepared, nine of which containing different proportions of coarse and/or fine RCA (0%, 50% or 100% for both fractions), and the tenth mixed with 100% coarse and fine RCA, and RCA powder 0–1 mm. The following properties were evaluated: compressive strength, modulus of elasticity, splitting tensile strength, flexural strength, and effective porosity as measured with the capillary-water-absorption test. Negative effects on the above properties were recorded for increasing contents of both RCA fractions. The application of simple regression models yielded porosity-based estimations of the mechanical properties of the SCC with an accuracy margin of ±20%, regardless of the RCA fraction and amount. The results of the multiple regression models with compressive strength as a secondary predictive variable presented even greater robustness with accuracy margins of ±10% and almost no significant effect of accidental porosity variations on prediction accuracy. Furthermore, porosity predictions using the 24-h effective water also yielded accurate estimations of all the above mechanical properties. Finally, comparisons with the results of other studies validated the reliability of the models and their accuracy, especially the minimum expected values at a 95% confidence level, at all times lower than the experimental results.

Spanish Ministry of Universities within the framework of the State Program for the Promotion of Talent and its Employability in R + D + i, State Mobility Subprogram, of the State Plan for Scientific and Technical Research and Innovation 2017–2020 [PRX21/00007]; the Spanish Ministry MCI, AEI, EU and ERDF [grant numbers PID2020-113837RB-I00; 10.13039/501100011033; FPU17/03374]; the Junta de Castilla y León (Regional Government) and ERDF [grant numbers UIC-231; BU119P17]; Youth Employment Initiative (JCyL) and ESF [grant number UBU05B_1274]; and finally, the University of Burgos [grant numbers SUCONS, Y135.GI] and the University of Padova.