In order to model the effect of mixture parameters and material properties on the hardened properties of, prestressed self-compacting concrete (SCC), and also to investigate the extensions of the statistical models, a factorial design was employed to identify the relative significance of these primary parameters and their interactions in terms of the mechanical and visco-elastic properties of SCC. In addition to the 16 fractional factorial mixtures evaluated in the modeled region of -1 to +1, eight axial mixtures were prepared at extreme values of -2 and +2 with the other variables maintained at the central points. Four replicate central mixtures were also evaluated. The effects of five mixture parameters, including binder type, binder content, dosage of viscosity-modifying admixture (VMA), water-cementitious material ratio (w/cm), and sand-to-total aggregate ratio (S/A) on compressive strength, modulus of elasticity, as well as autogenous and drying shrinkage are discussed. The applications of the models to better understand trade-offs between mixture parameters and carry out comparisons among various responses are also highlighted. A logical design approach would be to use the existing model to predict the optimal design, and then run selected tests to quantify the influence of the new binder on the model.
W. Long et al., "Factorial Design Approach in Proportioning Prestressed Self-Compacting Concrete," Materials, vol. 8, no. 3, pp. 1089-1107, MDPI AG, Mar 2015.
The definitive version is available at https://doi.org/10.3390/ma8031089
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Binders; Compressive strength; Concrete mixtures; Concretes; Design; Economic and social effects; Elasticity; Mechanical properties; Mixtures; Factorial design; Factorial design approach; Fractional factorials; Hardened properties; Model region; Statistical modeling; Viscoelastic properties; Water-cementitious material ratio; Self compacting concrete; Visco-elastic properties
International Standard Serial Number (ISSN)
Article - Journal
© 2015 MDPI AG, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.