A researcher from the Swinburne University of Technology and the director of French construction company Bouygues Travaux Publics, have used machine learning techniques to better understand the compressive strength of 3D printed construction materials.

Aiming to develop a process of classifying 3D printed geopolymer samples, the research team targeted specific variables, and optimized the makeup of the 3D printed materials using machine learning methods. The study could not only lead to the creation of construction composites exhibiting higher compressive strength, but also a roadmap for classifying the stability of other 3D printed compounds used in the construction industry.

“The aim is to introduce a feasible approach to classify geopolymer samples made via additive manufacturing techniques,” explained the team. “This study employed popular recursive partitioning functions including rpart and ctree, to build separate classification models. According to the findings, these functions demonstrate great ability to create models for 3D-printed geopolymers.