Abstract
The study reported in this paper is the first meta-analysis aimed at obtaining statistical models for the fresh state behavior of self-consolidating concrete (SCC) mixes which effectively reproduce the complex relationships between mix design and fresh state performance. A database compiled with data from more than 120 different sources was analyzed. This study proves that SCC fresh state performance is determined by three fundamental, uncorrelated properties: flow time, flow spread, and resistance to segregation, which constitute a robust mathematical framework for the optimization of SCC mixes. The models obtained for these fundamental properties have proved consistent and reproduce very well the general trends and interactions implicit in SCC mix design recommendations, which in effect constitute the mathematical validation of recommendations well sanctioned by practice. It has been proved that, if no supplementary cementitious materials (SCMs) are used, there is a remarkably narrow margin in which the three fundamental properties of fresh SCC mixes can be simultaneously optimized. The most stable mixes were found to be associated with sand-to-coarse aggregate ratios of at least 1.1. The flowability of SCC mixes in terms of both flow times and flow spread can be optimized when the following conditions concur: w/c ratio of 0.45, SCMs content below 100 kg/m3, and sand content not lower than 750 kg/m3. Furthermore, it was also proved that, in general, it is best to keep the dosages of superplasticizers (HRWRs) and viscosity-modifying agents (VMAs) below 1.7% and 0.7%, respectively, subject of course to variation across the different types of products available.
Highlights
Self-compacting or self-consolidating concrete (SCC) does not need any compaction and instead flows under its own weight, entirely fills the required formwork, and provides a homogeneous material upon placing [1,2,3,4]
It was proved that, in general, it is best to keep the dosages of superplasticizers (HRWRs) and viscosity-modifying agents (VMAs) below 1.7% and 0.7%, respectively, subject to variation across the different types of products available
Superplasticizers increase the workability of the mix [6, 7], whilst VMAs are often added to control the risk of segregation or, more generally, the stability of fresh SCC mixes [8]. e most salient differences between SCC and normal vibrated concrete in terms of mix design are lower coarse aggregate contents, increased paste content, lower water/powders ratio, an increased superplasticizer dosage, and the addition of VMAs when necessary [9]
Summary
Self-compacting or self-consolidating concrete (SCC) does not need any compaction and instead flows under its own weight, entirely fills the required formwork, and provides a homogeneous material upon placing [1,2,3,4]. Compressive strength methods are more recent and determine the proportions of SCC constituents based on compressive strength requirements, they require adjustments to all parameters in order to finalize the mix design [19] Paste rheology models, such as the one developed by Saak et al [22], was built on the assumption that the rheology of the paste affects the flowability and the resistance to segregation of the mix. Significant efforts have been made to try and rationalize the SCC proportioning process based on the application of statistical tools, mostly by fitting descriptive equations to a set of experimental results obtained from different fresh state tests [23,24,25,26] In most of these studies, the equations reported fitted very well the experimental results they were based upon, yielding extremely high R2 values. No research concerning SCC has been undertaken from this perspective. e study reported in this paper is the first of its kind concerning SCC, and it aimed at obtaining statistical models for the fresh state behavior of SCC which do not necessarily produce highly accurate predictions but rather are effective in reproducing the relationships between any SCC mix design and its fresh state performance and have general validity for use in the automated optimization of SCC mixes
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