Modeling fresh behavior of cement-based materials for 3-D concrete printing

Additive manufacturing of concrete materials has recently gained attention in the field of civil engineering. The construction industry has shown substantial interest in exploring the use of 3-D concrete printing as a supplement to conventional technology considering the reduced labor, formwork, and carbon emissions. A major impediment to the application of 3-D printable concrete lies in the design of the fresh material properties. Ideally, a fresh concrete needs to be fluid-like for pumping and extrusion but solid-like during placement to support the printed structure. 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 characterization 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. The best MLP model achieves an R-squared value of 0.981, outperforming the prediction accuracies in concurrent research. Additionally, both Discrete Element Method (DEM) and Smoothed Particle Hydrodynamics (SPH) are utilized to model the flow behavior of cement-based materials in common flow scenarios, with their simulation accuracy validated through rheometer simulations with deviations of 11.7% and 6.2%, respectively. SPH is further employed to simulate the printing behavior of different regimes of fresh cement-based materials, ranging from very fluid to less fluid, during the extrusion and deposition phases of 3D printing. These 3-D printing simulations provide insights into real-life issues such as jamming and plastic collapse. Vibration is shown to be effective in improving extrusion at nozzle and consistency during deposition. Machine learning and numerical simulation have proven their effectiveness in providing valuable insights into design of 3-D printing materials and construction processes. An innovative tool that integrates these techniques is envisioned, promising greater synergy in 3-D printing development.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Shen, Chuanyue

Contributors dc:contributor

• Lange, David
• Popovics, John
• Roesler, Jeffery
• Garg, Nishant
• Henschen, Jacob

• Cement-based Materials
• Fresh Behavior
• 3-d Printing
• Numerical Simulation
• Machine Learning

Statement dc:rights

• All rights reserved.

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124313

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124313