APPLICATIONS OF STATISTICAL MODELS IN PROPORTIONING MEDIUM-STRENGTH SELF-CONSOLIDATING CONCRETE

This paper reviews statistical models obtained from a factorial design carried out to determine the influence of 4 key parameters on filling and passing ability, segregation, and compressive strength. These parameters are important for successful development of medium-strength self-consolidating concrete (MS-SCC). The parameters considered were the contents of cement and pulverized-fuel ash (PFA), water-powder ratio (W/P), and dosage of HRWRA. The responses of the derived statistical models are slump flow, fluidity loss, rheological parameters, Orimet time, V-funnel time, L-box, JRing combined with Orimet, JRing combined with cone, fresh segregation, and compressive strength at 7, 28, and 90 days. The models are valid for mixtures made with 0.38-0.72 W/P, 60-216 kg/m3 of cement content, 183-317 kg/m3 of PFA, and 0-1% of high-range water-reducing admixture (HRWRA), by mass of powder. The utility of such models to optimize concrete mixtures to achieve good balance between filling ability, passing ability, segregation, compressive strength, and cost is discussed. Examples highlighting usefulness of the models are presented using isoresponse surfaces to demonstrate single and coupled effects of mixture parameters on slump flow, loss of fluidity, flow resistance, segregation, JRing combined with Orimet, and compressive strength at 7 and 28 days. Cost analysis is carried out to show tradeoffs between cost of materials and specified consistency levels and compressive strength at 7 and 28 days that can be used to identify economic mixtures. The paper establishes usefulness of the mathematical models as tools to facilitate the test protocol required to optimize MS-SCC.