Published October 2017 | Version v1
Journal article

Predictive modelling of hydration and mechanical performance of low Ca composite cements: Possibilities and limitations from industrial perspective

Description

We quantified hydration and performance development of composites cements containing metakaolin in the rage of 0–40% and investigated applicability of thermodynamic and micromechanical models to predict the evolution of hydrates assemblage and of performance, respectively. The reaction of metakaolin results in a formation of significant amounts of C-S-H phase for lower metakaolin content and of strätlingite for higher contents. This leads to very dense matrix with finer porosity allowing co-existence of thermodynamically incompatible phases, namely portlandite and strätlingite. Hence, thermodynamic modelling cannot be applied to correctly predict the phase assemblage for the systems with high metakaolin content. By introducing constraints into the thermodynamic model in a form of minimum phase content, the XRD-amorphous phases can be quantified. However, such constraints are only valid for the cases studied and limit the general applicability and predictive power of the model. Elastic properties and strength of mortars are influenced by the metakaolin additions in an opposite trend. It indicates that they are sensitive to different microstructural features/mechanisms. Continuum micromechanic-based model is capable to accurately predict the performance evolution based on the volume of hydrates predicted by the thermodynamic modelling. An additional step required for the accurate prediction - porosity is partitioned based on its saturation at 95% RH – is introduced and substantiated.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.cemconres.2017.05.020

Additional details

Identifiers

DOI
10.1016/j.cemconres.2017.05.020;
PII
S0008-8846(16)30940-1;

Publishing Information

Journal Title
Cement and Concrete Research
Journal Volume
100
Journal Page Range
p. 68-83
ISSN
0008-8846
CODEN
CCNRAI

Optional Information

Copyright
Copyright (c) 2017 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.