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Statistical models for the development of optimized furnace slag lightweight aggregate self-consolidating concrete
Abstract This investigation aims to develop robust lightweight self-consolidating concrete (LWSCC) mixtures with furnace slag (FS) aggregates. A factorial design was carried out to mathematically model the influence of three key mix design parameters on fresh and hardened properties of furnace slag LWSCC (FS-LWSCC) mixtures. The parameters were water to binder ratio (w/b), high range water reducing admixture (HRWRA) content and total binder content. The responses of the derived statistical models were slump flow, V-funnel flow time, J-ring flow diameter/height difference, L-box ratio, filling capacity, sieve segregation, unit weight and compressive strength. Twenty mixes were prepared to derive the statistical models, and ten mixes were used to verify the accuracy of the developed models. The models were valid for mixes made with 0.30–0.40 w/b, HRWRA of 0.3–1.2% (by total binder content), and 410 to 550kg/m3 of total binder content. The influences of w/b, HRWRA% and total binder content were characterized and analyzed using regression technique to identify the primary factors and their interactions on the measured properties. Utilizing the developed models, three optimum FS-LWSCC mixtures with high statistical desirability were formulated and tested. It was possible to produce robust FS-LWSCCs that satisfy European EFNARC criteria.
Statistical models for the development of optimized furnace slag lightweight aggregate self-consolidating concrete
Abstract This investigation aims to develop robust lightweight self-consolidating concrete (LWSCC) mixtures with furnace slag (FS) aggregates. A factorial design was carried out to mathematically model the influence of three key mix design parameters on fresh and hardened properties of furnace slag LWSCC (FS-LWSCC) mixtures. The parameters were water to binder ratio (w/b), high range water reducing admixture (HRWRA) content and total binder content. The responses of the derived statistical models were slump flow, V-funnel flow time, J-ring flow diameter/height difference, L-box ratio, filling capacity, sieve segregation, unit weight and compressive strength. Twenty mixes were prepared to derive the statistical models, and ten mixes were used to verify the accuracy of the developed models. The models were valid for mixes made with 0.30–0.40 w/b, HRWRA of 0.3–1.2% (by total binder content), and 410 to 550kg/m3 of total binder content. The influences of w/b, HRWRA% and total binder content were characterized and analyzed using regression technique to identify the primary factors and their interactions on the measured properties. Utilizing the developed models, three optimum FS-LWSCC mixtures with high statistical desirability were formulated and tested. It was possible to produce robust FS-LWSCCs that satisfy European EFNARC criteria.
Statistical models for the development of optimized furnace slag lightweight aggregate self-consolidating concrete
Lotfy, Abdurrahmaan (Autor:in) / Hossain, Khandaker M.A. (Autor:in) / Lachemi, Mohamed (Autor:in)
Cement and Concrete Composites ; 55 ; 169-185
06.09.2014
17 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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