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Dissertation Proposal - Tianjie Zhang

April 14, 2023 @ 10:00 am MDT

Understanding Rheology and Non-Newtonian Flow Behaviors of Cementitious Materials Using Physics-informed Neural Network

Presented by Tianjie Zhang – Data Science emphasis

Virtual – Click here to join via Zoom


Cementitious materials, essentially a mixture of cement and water, have become the most commonly used infrastructural material in modern civilizations. Figuring out the flowability and workability of fresh concrete is significantly important in construction works. The modeling of rheology and flow behaviors of cementitious materials has attracted a lot of research interest these years in a variety of application areas, such as pumpable mortar grouting, self-consolidated concrete, and the promising 3D digital construction of concrete. Despite a great number of modeling and experimental investigations of the flow behaviors of cementitious materials, there is a certain gap between a practical industrial approach and fundamental rheological studies. In the last five-decade years, there has been a tremendous progress in solving the rheological constitutive equation and Navier-Stokes equations numerically using Finite Difference Method (FDM) and Finite Element Analysis (FEA) to simulate the behavior of cementitious materials. However, solving these problems and predicting the properties or the flow of the material using physical simulation methods is often computationally time-consuming and sometimes even error-prone. Therefore, in this work, we aim to present reliable and accurate simulation frameworks for further understanding the rheology and flow behavior of cementitious materials. To carry out this goal, three different PINNs will be constructed in this proposal to develop a comprehensive view of rheology and localize the shear strain in fluids as well as accurately and fast predict the velocity and pressure fields of cementitious materials. This work can guide the cement-based materials’ transporting, compacting and placing, especially for cement-based 3D printing procedures. In addition, it can also be used in predicting other thixotropic fluids like ketchup, toothpaste, margarine or shaving cream.


Dr. Yang Lu (Chair), Dr. Tim Andersen, Dr. Eric Henderson