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Three-dimensional (3D) printing has been used in various fields to tackle applications difficult for conventional manufacturing. To realize the full potential of this technology in the transportation sector, it is imperative to identify suitable tests and mixtures for printing “inks” made of mortar. In this study, several conventional and non-conventional tests on mortars and cement pastes were conducted. This work highlights the correlation between the results of slump test and the deformation test that indicates the comportment of the mixture under a stack of printed layers. Moreover, a strong connection between yield stress and mini-slump is observed, demonstrating a simplification of the testing procedure, and a link between the mortar and the cement paste is developed. In the printing ink design phase, this association enables the prediction of flowability. The yield stress and the final radius of the mini-slump tests were very well correlated for the admixture tested. The use of the mini-slump test simplifies the testing procedure and allows for quicker formulations of admixtures in the printing ink.

The 3D printing of cementitious material requires an understanding of the printing material’s rheological properties, especially yield stress and viscosity. The ability to adjust and control these properties in the 3-D printing process represents a breakthrough for the construction sector. The aim of this paper is to experimentally determine the dynamic yield stress and static yield stress of cementitious materials with admixtures. Unlike conventional rheometer geometries, the National Institute of Standard and Technology (NIST) spindle geometry is used in this work for the measurement of both yield stresses. While dynamic yield stress was measured by increasing and decreasing the rotational velocity, the static yield stress was characterized by two methods: the torque increase method and the constant rotational speed method. For both yield stresses, a total of 16 mixtures were studied in a factorial experimental design of the chemical admixtures. The impact of each admixture has been highlighted. The structural build up was assessed through the structuration rates. The temperature effect on the early age properties was studied for the most promising formulations.

The development of printable cement-based materials is a high priority in the field of 3D printing for construction. There are many admixtures available for the design of the printing mortar ink which can influence the wet and final properties of the mortar. In this work, artificial intelligence has been utilized to predict those properties and guide the dosage of each admixture. The algorithms were developed from a factorial experimental plan. The mortar investigated consists of cement blended with silica fume to reduce the embodied carbon of the mixture. The selected admixtures were a superplasticizer, a viscosity modifying agent, nano-clay, C-S-H seeds and an accelerator with a water-reducing effect. A rotary rheometer was used to measure the viscosity and the dynamic yield stress of both mortar and cement-paste mixtures. Additional tests were conducted such as the small Abrams cone and the ASTM C1437 flow test. Several predictive algorithms were developed and compared, in which artificial neural networks were used. Furthermore, to enhance the performance of the neural network, a genetic algorithm was used to optimize the network parameters. To evaluate the performance of the models, the normalized root mean square error (NRMSE), and coefficient of determination (R2) were calculated. This approach is a single-objective prediction which yields promising capability to predict the wet properties of both mortar and cement pastes, which can be later expanded into a multi-objective approach.