New aggregate grading models for low-binder self-consolidating and semi-self-consolidating concrete (Eco-SCC and Eco-semi-SCC)

In this study, the characteristics of the granular skeletons of 38 low-binder self-consolidating concrete mixtures were investigated to minimize the binder content and volume of paste (VP). 20 granular mixtures were proportioned using 5 different Fuller-Thompson distribution moduli (q) of 0.4 to 0.5 and 4 different volumetric sand-to-total aggregate ratios (S/A) of 0.45-0.60. The investigated Eco-SCC mixtures with a water-to-binder ratio (w/b) of 0.60 and VP of 29% were classified based on their flowability, passing ability, and static stability results. The volume of paste was reduced in two successive steps to 26% and 24% using the Eco-SCC mixtures proportioned with the optimized granular skeletons. Using 3D image analysis of the aggregate shapes, modified versions of Funk-Dinger model and new grading models as functions of natural logarithm of the particle sizes were proposed for the optimized granular skeletons. The new proposed models were validated with the Eco-SCC mixtures containing the minimum VP of 24% and 2 new aggregate sources. The results showed that considering the realistic dimensions of aggregate using the new proposed logarithmic grading model with q of 2.6212 and S/A of 0.55 can result in a significant reduction in binder content and volume of paste from 309 kg/m(3) to 256 kg/m(3) and 29% to 24%, respectively, with equivalent workability performance. These also led to decrease the global warming potential values from 206 to 172 e-CO2 kg/m(3) of concrete, while higher eco-efficiency indices and workability characteristics, as well as an appropriate compressive strength for housing application were achieved. (C) 2020 Elsevier Ltd. All rights reserved.