Performance-Based Concrete as a Route for CO2 reduction

Concrete is the most widely used material in today's construction industry. It is estimated that 90% of concrete's
greenhouse gas emissions can be attributed to cement production. Cement production is the source of approximately 7%
of the world's CO2 emissions. Therefore, a rapid transition to lower-cement concrete is imperative. One of the methods
for improving the sustainability of concrete production is the use of Supplementary Cementitious Materials (SCMs) to
replace cement in concrete mixtures. One of the most significant barriers to the broader use of SCMs is that it is
challenging to accurately predict the resulting concrete's properties. This research project will aim to better understand
the physical and chemical characteristics of selected SCMs through novel testing methods like electron microscopy
combined with EDS microchemical analysis and to establish links between those characteristics and the performance of
the produced concrete. In addition, a machine-learning model will be developed and trained to predict the strength and
durability properties of blended concrete and to identify the optimum SCM characteristics for maximising cement
replacement. The results of the machine-learning model will also be used to create empirical formulas that can be used
to predict properties of the produced concrete based on SCM characteristics as well as mix composition. This research
will give concrete designers more confidence in using blended concrete mixes.