Rodriguez, Jonathan F.. Creep behavior of fiber reinforced self consolidating concrete reinforced with hybrid fibers. Retrieved from https://doi.org/doi:10.7282/t3-738r-pp78
DescriptionCreep and shrinkage properties of fiber reinforced self-consolidating concrete (FR-SCC) are still unknown and limited in research. Thus, comprehensive evaluation is required to determine these behaviors of FR-SCC and mechanical properties must be verified to determine effectiveness of this type of concrete in structural application.
The objective of the research is evaluation of the influence of micro/macro polypropylene and steel fibers on creep and shrinkage of self-consolidating concrete. (8) mixes are performed with recommended dosages of fiber content; and two mixes will be hybrid combination of the fibers with appropriate fiber dosages. Concrete specimens were also evaluated for fresh concrete property testing such as slump, j-ring, visual stability index, T20 and air content (pressure method) and for mechanical properties such as compressive strength, tensile strength, elastic modulus, free shrinkage, flexure, and rapid chloride permeability testing.
Experimental data for creep and shrinkage were also compared to the following prediction models: ACI209, B3, CEB MC90-99. and GL2000 to determine which model most accurately predicts the behavior of these FR-SCC mixes. Results show that a combination of micro and macro polypropylene fibers cause the most reduction in shrinkage but also cause the most increase in creep strain compared to non-fiber-reinforced self-consolidating concrete. Furthermore, polypropylene fibers of 1.5'' length cause the highest increase in specific creep while steel crimped fibers of 1.5'' length cause the lowest increase in specific creep. Finally, Bazant-Baweja B3 Model is the most accurate model in predicting creep behavior for the FR-SCC mixes while CEB90-99 is the most accurate for predicting shrinkage behavior for FR-SCC mixes. A correction equation is implemented for the Bazant B3 creep model to increase accuracy of prediction to experimental data.