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Statistical models to predict fresh and hardened properties of self-consolidating concrete
Abstract Several material properties and mix design parameters affect the performance of self-consolidating concrete (SCC) and need to be taken into consideration to enhance the fresh and hardened properties of the concrete. A factorial design was conducted to model the effect of mixture parameters and material properties on workability, mechanical properties, and visco-elastic properties of SCC used for the construction of precast/prestressed structural elements. The modeled mixture parameters included the binder content, binder type, water-to-cementitious materials ratio, sand-to-total aggregate ratio (S/A), and dosage of thickening-type viscosity-modifying admixture. In total, 16 SCC mixtures were investigated to establish a factorial design with five main factors. Three replicate SCC mixtures were prepared to estimate the degree of the experimental error for the modeled responses. The mixtures were evaluated to determine several key responses that affect the performance of precast, prestressed concrete, including the filling ability, passing ability, filling capacity, stability, compressive strength, modulus of elasticity, flexural strength, autogenous shrinkage, drying shrinkage, and creep. The derived statistical models enable to quantify the level of significance of each of the five investigated parameters on fresh and hardened properties of SCC, which can simplify the test protocol needed to optimize SCC. Based on the results derived from the factorial design, recommendations for the proportioning of SCC in terms of workability, mechanical properties, and visco-elastic properties are given.
Statistical models to predict fresh and hardened properties of self-consolidating concrete
Abstract Several material properties and mix design parameters affect the performance of self-consolidating concrete (SCC) and need to be taken into consideration to enhance the fresh and hardened properties of the concrete. A factorial design was conducted to model the effect of mixture parameters and material properties on workability, mechanical properties, and visco-elastic properties of SCC used for the construction of precast/prestressed structural elements. The modeled mixture parameters included the binder content, binder type, water-to-cementitious materials ratio, sand-to-total aggregate ratio (S/A), and dosage of thickening-type viscosity-modifying admixture. In total, 16 SCC mixtures were investigated to establish a factorial design with five main factors. Three replicate SCC mixtures were prepared to estimate the degree of the experimental error for the modeled responses. The mixtures were evaluated to determine several key responses that affect the performance of precast, prestressed concrete, including the filling ability, passing ability, filling capacity, stability, compressive strength, modulus of elasticity, flexural strength, autogenous shrinkage, drying shrinkage, and creep. The derived statistical models enable to quantify the level of significance of each of the five investigated parameters on fresh and hardened properties of SCC, which can simplify the test protocol needed to optimize SCC. Based on the results derived from the factorial design, recommendations for the proportioning of SCC in terms of workability, mechanical properties, and visco-elastic properties are given.
Statistical models to predict fresh and hardened properties of self-consolidating concrete
Long, Wu-Jian (author) / Lemieux, Guillaume (author) / Hwang, Soo-Duck (author) / Khayat, Kamal Henri (author)
Materials and Structures ; 45 ; 1035-1052
2012-03-09
18 pages
Article (Journal)
Electronic Resource
English
Self-consolidating concrete , Statistical models , Fresh properties , Mechanical properties , Visco-elastic properties Engineering , Materials Science, general , Building Materials , Operating Procedures, Materials Treatment , Civil Engineering , Structural Mechanics , Theoretical and Applied Mechanics
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