Geometric Conformability of 3D Concrete Printing Mixtures from a Rheological Perspective.

The effectiveness of 3D concrete printing (3DCP) relies on understanding the rheological properties of cementitious materials and their time-dependent evolution. These materials exhibit shear-thinning viscosity, an elastic region, and both static and dynamic yield stress, which are challenging to balance in 3DCP. Layer deformation can be caused by factors such as self-weight, the weight of subsequently deposited layers, and the stress induced by the nozzle pressing. Starting at the level of a single filament, the final geometrical conformity of a 3D-printed object is the sum of individual filament conformities. Hence, the control of layer deformation during the printing process is critical. The failure of 3D-printed objects can occur due to two primary mechanisms: material failure, which occurs when the material's strength is exceeded, resulting in fracture or uncontrolled deformation; and stability failure, where the object cannot retain equilibrium of forces. These mechanisms often interact; extensive deformations resulting from material failure can lead to stability loss, or conversely, stability loss generates local excessive stresses leading to material failure. The governing mechanism depends on various factors, including material and process characteristics, as well as the transient nature of material properties, print strategy, and object design. With this in mind, this research aimed to broaden the understanding of the connection between rheological material properties-primarily yield stress-and the geometric conformability of printed objects. Experimental tests were conducted on pastes using a rheometer, and correlated mortars, allowing for the evaluation of realistic extrusion properties.

Probability Density Function Models for Float Glass under Mechanical Loading with Varying Parameters.

Glass as a construction material has become indispensable and is still on the rise in the building industry. However, there is still a need for numerical models that can predict the strength of structural glass in different configurations. The complexity lies in the failure of glass elements largely driven by pre-existing microscopic surface flaws. These flaws are present over the entire glass surface, and the properties of each flaw vary. Therefore, the fracture strength of glass is described by a probability function and will depend on the size of the panels, the loading conditions and the flaw size distribution. This paper extends the strength prediction model of Osnes et al. with the model selection by the Akaike information criterion. This allows us to determine the most appropriate probability density function describing the glass panel strength. The analyses indicate that the most appropriate model is mainly affected by the number of flaws subjected to the maximum tensile stresses. When many flaws are loaded, the strength is better described by a normal or Weibull distribution. When few flaws are loaded, the distribution tends more towards a Gumbel distribution. A parameter study is performed to examine the most important and influencing parameters in the strength prediction model.