This research is focused on investigating the potential use of by-product waste and natural materials for improving alternative green cement mixes. The primary objective of this research is to expand the use of sustainable and more environmentally friendly cement alternatives, by enhancing their performance for construction applications. To activate the binding properties of binder under investigation, they have to be mixed with an alkaline solution in specific quantities. To broaden the use of these binders in the construction industry, parameters such as the effect of different chemical activators types and dosages, curing times and temperatures, processing techniques, and the chemical and physical composition of the source material have to be studied. The impact of those parameters will be co-related to the binder\u27s fresh and hardened performance, mainly in terms of its mechanical and rheological properties. A bottom-up multiscale characterization scheme will be conducted to study the engineered binders\u27 physical and chemical properties. Once the results are obtained, the mix-design guidelines will be published.
Advancing materials for construction applications will generate large databases targeting specific design demands. Utilizing “Design of Experiment” (DOE) and machine learning tools such as Artificial Neural Network (ANN) will speed up the optimization methods and will reduce the cost and time for introducing new construction materials. Therefore, an ANN model is developed that can predict properties of interest for different binding mixtures. The developed models can be used for tailoring mixes for general construction and 3D printing processes. The fresh and hardened properties used in the ANN models are obtained from experimental measurements. After training the models, tests are performed experimentally, and the results compared to the model outputs are used to validate the model
