Abstract
Durability of 3-D printable concrete begins with control of flow and consolidation of the fresh material. 3D printed concrete needs to be fluid-like for pumping and extrusion but solid-like during placement to support the printed structure. High quality 3D printed materials must have excellent consolidation and a low volume of trapped voids. In practice, achieving this balance is challenging. This research addresses these challenges by leveraging machine learning and numerical simulation to model the fresh behavior of cement-based materials for 3-D printing. It begins with rheological characterisation to understand how mixture design factors influence rheological properties, laying the foundation for subsequent studies. A multilayer perception (MLP) model is developed to predict the yield stress of cement-based materials, achieving a desired overall accuracy while demonstrating performance variations on some subsets of data. Additionally, both Discrete Element Method (DEM) and Smoothed Particle Hydrodynamics (SPH) are utilised to model the flow behavior of cement-based materials in common flow scenarios. These 3-D printing simulations help us understand real-life issues such as jamming and plastic collapse. Vibration of the material in the printer nozzle was shown to be effective for improving extrusion and consistency during deposition. Machine learning and numerical simulation provided insights into design of 3-D printing materials and construction processes. This study found that computer modeling was able to capture behaviour, and thus serve as a design tool for new 3-D printable mixtures.
Keywords
3D printing concrete, fresh properties, fluid behavior, machine learning, numerical modeling.
DOI
10.5703/1288284318166
Recommended Citation
Lange, David A. and Shen, Chenyue, "Toward Durable 3D Printed Structures" (2025). International Conference on Durability of Concrete Structures. 7.
https://docs.lib.purdue.edu/icdcs/2025/keynote/7
Toward Durable 3D Printed Structures
Durability of 3-D printable concrete begins with control of flow and consolidation of the fresh material. 3D printed concrete needs to be fluid-like for pumping and extrusion but solid-like during placement to support the printed structure. High quality 3D printed materials must have excellent consolidation and a low volume of trapped voids. In practice, achieving this balance is challenging. This research addresses these challenges by leveraging machine learning and numerical simulation to model the fresh behavior of cement-based materials for 3-D printing. It begins with rheological characterisation to understand how mixture design factors influence rheological properties, laying the foundation for subsequent studies. A multilayer perception (MLP) model is developed to predict the yield stress of cement-based materials, achieving a desired overall accuracy while demonstrating performance variations on some subsets of data. Additionally, both Discrete Element Method (DEM) and Smoothed Particle Hydrodynamics (SPH) are utilised to model the flow behavior of cement-based materials in common flow scenarios. These 3-D printing simulations help us understand real-life issues such as jamming and plastic collapse. Vibration of the material in the printer nozzle was shown to be effective for improving extrusion and consistency during deposition. Machine learning and numerical simulation provided insights into design of 3-D printing materials and construction processes. This study found that computer modeling was able to capture behaviour, and thus serve as a design tool for new 3-D printable mixtures.
