Multi-objective optimization of a sustainable ternary mortar for 3D printing

The construction industry is compelled to evolve toward industrial-ized and sustainable means and processes. Indeed, the Portland cement produc-tion alone is responsible for up to 8 % of global greenhouse gas emissions. In this context, concrete 3D printing demonstrates a significant potential for the reduction of material use. However, the majority of 3D printing materials dis-play a high cement content due to the rheological constraints associated with 3D printing along with the complexity of mix design.
The present study proposes to integrate a parametric life cycle assessment cal-culation in the multi-objective optimization of a 3D printing mortar in order to minimize the environmental impact with a significantly reduced mix design workload. Applied to a limestone calcined clay blend, a genetic algorithm is used to decrease the climate change score while maintaining a set of rheological printability criteria predicted by artificial neural networks. As a result, this methodology allows the identification of locally optimized mixtures (up to 82wt% cement replacement) in as low as 9 additional formulations. Besides, as the process advances for an expanded design region, its efficiency is enhanced.
This methodology is reproducible with locally sourced materials and for the majority of 3D printing materials, which are usually designed through empirical trial and error. This study introduces a systematic optimization process which establishes the sustainability at the core of its objectives and includes innovative tools for the formulation of cementitious materials.