3D Concrete Printing Material Prediction and Flow Simulation Using
Physics-Informed Neural Network
Authors: Zhang Tianjie 
Published: February 2025

3D concrete printing (3DCP) is an innovative construction method that extrudes
cementitious materials layer-by-layer to fabricate building components based on
a digital model. 3DCP has gained increasing adoption globally for projects like
buildings, bridges, retaining walls, and stormwater management systems. However,
3DCP places stringent demands on the rheological properties of the printable
cementitious materials. Rheology is critical for the flow and buildability of
fresh concrete during extrusion. The mixture must have a low yield stress for
pumping, moderate viscosity to hold its shape after deposition, minimal bleeding
and segregation of aggregates, as well as responsive rheology that can
transition from fluid to solid state as layers are printed sequentially.

In this dissertation, we aim to solve the challenges mentioned above including:
(1) accurate and efficient quantification of rheological and thixotropic
properties of cementitious materials, (2) evaluation of flow behavior of fresh
concrete during extrusion.

Using traditional experimental methods to evaluate rheological properties of
cement paste is labor-intensive and time-consuming. Numerical simulations like
Finite Element Method (FEM) or Computational Fluid Dynamics (CFD) can help
increase the speed of finding the most suitable material for 3DCP. However,
these simulation methods are costly in computing power, making them less
desirable for simulating the dynamic and complex process of 3DCP. Moreover,
mesh-based approaches are not suitable for complex geometries as the mesh
generation would be challenging with curved boundaries, small features, holes or
thin regions. Physics-Informed Neural Network (PINN) is introduced in the work
as an innovative solution to simulate and model the whole process of 3DCP. It
can leverage the flexibility and computational efficiency of data-based neural
networks, which can accelerate the learning and convergence speed. At the same
time, Partial Differential Equations (PDEs) would be embedded in the network
structure to increase the accuracy of its prediction. The integrated governing
physical laws can help the neural network understanding the underlying physics
of the 3DCP process, potentially increasing the accuracy and reliability of
simulations.

A significant part of the research is dedicated to understanding the rheological
and thixotropic behavior of cementitious materials, which is critical for the
material design in 3DCP. We developed a Rheology-informed Neural Network,
RheologyNet, where we embedded the rheological constitutive laws into the loss
function. These physical laws can help the network focus on simulating the
rheological properties of cementitious materials. Moreover, a thixotropic
evaluation system has been established based on the proposed model to evaluate
the thixotropic property of cement paste. This study is presented in Chapter
2.

Then, we proposed a Navier-Stokes Informed Neural Network (NSINN) to study the
flow behavior of cement paste in the 3DCP barrel and nozzle since it involves
complicated Multiscale or Multiphysics behaviors, such as anisotropy and
non-uniform shear rate distribution. The Navier Stokes Equation and the
rheological constitutive equations are coupled and embedded into the NSINN
architecture to simulate the flow behavior in the barrel during 3D-printing
extrusion process. This approach demonstrates the potential of PINNs to provide
high-accuracy simulations of velocity and pressure fields in a computationally
efficient manner compared to traditional mesh-based models. Also, we used the
NSINN to learn the relationship between the nozzle size and the printing
quality. The details of the study are presented in Chapter 3.

At last, a multi-subnetwork PINN architecture (MultiSubPINN) is proposed to
address the limitations of traditional PINNs. This is because traditional PINNs
used Fully-connected Neural Network (FNN) as main bone. It has problems like
different outputs must share parameters within the network structure. However,
this parameter sharing nature might lead to potential errors and decrease the
model performance. The main architecture of this network is consisted of
multiple separated sub-networks and the loss function of each sub-network is
calculated separately to update the parameters in each sub-network. By doing
this, we can ensure that the optimization for one output doesn't negatively
impact others, therefore providing a more flexible, scalable, and potentially
more accurate alternative for the evaluation. Besides, using randomly
distributed training points in the computational domain for PINNs is a common
way. However, many physical problems exhibit non-uniform behavior, with certain
regions of the domain having more complex dynamics or sharper gradients than
others. Randomly distributed training points might not adequately capture these
regions, leading to a model that misrepresents the underlying physics in
critical areas. We proposed a Physical-based sampling strategy to optimize the
training of the PINNs. This study is presented in Chapter 4.

In summary, this dissertation has established a new platform that is enabled by
interpretable NN to accurately quantify cement paste’s rheological properties
and simulate the flow behavior of 3D concrete printing process. The findings
suggest that PINNs can not only improve the efficiency and accuracy of
simulations but also provide a more profound understanding of the material
behaviors and process dynamics critical to 3DCP. The novel PINN structure
developed from this dissertation is a powerful general platform that could be
applicable to a variety of material domains for complex rheological behavior
predictions.