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A platform for research: civil engineering, architecture and urbanism
Utility of statistical models in proportioning self-consolidating concrete
Abstract In addition to sound material selection, the mix design of self-consolidating concrete requires careful tailoring of mixture constituents to secure a proper balance between contradictory properties necessary for the successful production of such a complex material. Mixture optimization of self-consolidating concrete often requires several trial batches to secure the required characteristics. This paper reviews statistical models developed using a factorial design approach to understand the effect of mixture parameters on key responses, including slump flow, rheological parameters, filling capacity, V-funnel flow time, surface settlement, and compressive strength. The models are valid for mixtures with 0.37 to 0.50 W/CM, 360 to 600 kg/$ m^{3} $ of binder, 240 to 400 l/$ m^{3} $ of coarse aggregate, 0.05 to 0.20% of viscosity-enhancing agent, by mass of water, and 0.3 to 1.1% of high-range water reducer, by mass of binder. Although the predicted response changes with the deviation from material characteristics used in establishing the models, the models remain quite useful in determing the significance of mixture parameters and their interactions on self-consolidating concrete properties. This paper demonstrates the usefulness of the models in establishing trade-offs among mixture parameters necessary for mixture optimization and compares the effect of changes in such parameters on key self consolidating concrete responses. The utility of the models to establish correlation between different workability characteristics useful for quality control is also highlighted.
Utility of statistical models in proportioning self-consolidating concrete
Abstract In addition to sound material selection, the mix design of self-consolidating concrete requires careful tailoring of mixture constituents to secure a proper balance between contradictory properties necessary for the successful production of such a complex material. Mixture optimization of self-consolidating concrete often requires several trial batches to secure the required characteristics. This paper reviews statistical models developed using a factorial design approach to understand the effect of mixture parameters on key responses, including slump flow, rheological parameters, filling capacity, V-funnel flow time, surface settlement, and compressive strength. The models are valid for mixtures with 0.37 to 0.50 W/CM, 360 to 600 kg/$ m^{3} $ of binder, 240 to 400 l/$ m^{3} $ of coarse aggregate, 0.05 to 0.20% of viscosity-enhancing agent, by mass of water, and 0.3 to 1.1% of high-range water reducer, by mass of binder. Although the predicted response changes with the deviation from material characteristics used in establishing the models, the models remain quite useful in determing the significance of mixture parameters and their interactions on self-consolidating concrete properties. This paper demonstrates the usefulness of the models in establishing trade-offs among mixture parameters necessary for mixture optimization and compares the effect of changes in such parameters on key self consolidating concrete responses. The utility of the models to establish correlation between different workability characteristics useful for quality control is also highlighted.
Utility of statistical models in proportioning self-consolidating concrete
Khayat, K. H. (author) / Ghezal, A. (author) / Hadriche, M. S. (author)
2000
Article (Journal)
English
Utility of statistical models in proportioning self-consolidating concrete
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