Porosity effects on rheological and mechanical behavior of self-compacting concrete

• Published in: Journal of Building Engineering Vol. 48; p. 103964
• Main Authors: Benaicha, Mouhcine, Jalbaud, Olivier, Hafidi Alaoui, Adil, Burtschell, Yves
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2022
• Subjects: Air content; Compressive strength; Regression model; Rheological parameters; Self-compacting concrete; Sonic velocity; Self-compacting concrete; Compressive strength; Air content; Sonic velocity; Regression model; Rheological parameters
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2021.103964

Unlike ordinary concrete, self-compacting concrete (SCC) is a concrete exceptionally fluid throughout the flow process, without reducing the strength of the material once in place. To reduce bleeding and segregation, admixtures and other modifiers are often used. About this and for improving the resistance of mixture to freezing and thawing, higher air contents may be required. In this study, twenty SCC mixes were designed based on AFGC and EFNARC recommendations. An air-entraining admixture (AEA) was added in several percentages, namely in 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95 and 1% of cement weight. Experimental studies have been performed to examine the rheological and mechanical characteristics of SCC's, such as density, plastic viscosity, yield stress, slump flow diameter, L-Box ratio, V-Funnel flow time, sieve stability segregation, Modulus of Elasticity at 28 days, and compressive strength and sonic velocity (at 1, 7, and 28 days) of the SCC using different air content. However, the linear and polynomial regression models between these parameters are proposed. The statistics coefficients such as “Adj. R-Square”, "Pearson's r", “R-Square” and “standard error” are also indicated whose aim is to determine the best model to adopt for the prediction of rheological and mechanical behavior of SCC according to the intrinsic characteristics of the mixture. As the percentage of AEA in the mixture increases, the values of air content, void ratio, water absorption, slump flow diameter, and L-Box ratio increase as well. Conversely to this, the values of fresh and hardened density as well as plastic viscosity, yield stress, V-Funnel flow time, sieve stability, compressive strength and modulus of elasticity as well as sonic velocity decrease as AEA percentage rises. •Prediction of rheological and mechanical characteristics of SCC.•The use of air-entraining admixture to increase air content in SCC.•Improving the resistance of mixture to freezing and thawing.•The use of linear and polynomial regression models.