Coupling machine learning with thermodynamic modelling to develop a composition-property model for alkali-activated materials

Xinyuan Ke, Yu Duan

Research output: Contribution to journalArticlepeer-review


Alkali-activation is one of the most promising routes for utilisation of versatile aluminosilicate resources. However, the variations of chemical compositions in these resources have increased the challenge of designing alkali-activated materials (AAMs) with multiple sources, posing the demand for establishing composition-property correlations that can represent a wide range of AAMs. This study proposes a data-driven approach to develop such composition-property correlations combining machine learning with global sensitivity analysis and thermodynamic modelling. The strength performance of alkali-activated concretes was investigated for a benchmark study (196 data inputs). The impact of the five key chemical compositions, CaO–SiO2–Al2O3–MgO–Na2O, has been assessed. The results show that despite the use of different aluminosilicate precursors, there appear to be coherent connections between bulk binder chemical compositions, phase assemblages, and the performance of AAMs. The composition-property correlations established via machine learning can be used to facilitate the on-demand design of AAMs utilising varying aluminosilicate resources.

Original languageEnglish
Article number108801
JournalComposites Part B: Engineering
Early online date31 Mar 2021
Publication statusE-pub ahead of print - 31 Mar 2021


  • Alkali-activated materials
  • Mechanical performances
  • Sobol global sensitivity indices
  • Thermodynamic modelling
  • Weighted Gaussian processes

ASJC Scopus subject areas

  • Ceramics and Composites
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Cite this